Doktorand - Lediga jobb

Arbetsuppgifter

Tjänsten omfattar både experimentella och numeriska studier inom brottmekanik. Det experimentella arbetet fokuserar på att utveckla nya provgeometrier för att studera utveckling av defekter, speciellt hålbildning, under ett brett spektrum av spänningstillstånd, med hjälp av avancerad röntgenbaserad datortomografi (CT) i både avbrutna och in situ-försök. Inledande studier genomförs med institutionens egen micro-CT-utrustning och utökas senare till synkrotronexperiment i samarbete med nationella och internationella partners. De experimentella resultaten kommer sedan användas för att utveckla förbättrade modeller för skadeutveckling i kontinummbaserade ickelokala materialmodeller, anpassade för implementering i finita element-modeller för analys av brott. Dessa modeller kommer att tillämpas för att studera övergången mellan segt och sprött brott (ductile-to-brittle transition) i HSLA-stål.

Kvalifikationer

Krav

• Avlagd doktorsexamen eller utländsk examen som bedöms motsvara en doktorsexamen. Detta behörighetskrav ska vara uppfyllt senast vid tidpunkten då anställningsbeslutet fattas.
• Utöver dina vetenskapliga färdigheter och pedagogiska förmåga, är en relevant doktorsavhandling önskvärd, internationell erfarenhet och kunskaper i engelska nödvändiga eftersom det behövs i det dagliga arbetet.
• Vi söker en mycket motiverad och samarbetsinriktad postdoktor med en stark bakgrund inom experimentell och beräkningsbaserad brottmekanik. Vi förväntar oss att kandidaten kan arbeta självständigt, presentera och sprida sina forskningsresultat vid internationella konferenser och genom vetenskapliga publikationer i högt rankade tidskrifter samt uppvisa hög vetenskaplig noggrannhet och integritet. Rollen innebär nära samarbete med kollegor från olika discipliner, stöd till doktorander samt förmåga att analysera och hantera komplexa utmaningar på ett professionellt sätt.

Meriterande

• Vid sista ansökningsdag högst tre år sedan doktorsexamen eller en utländsk examen som bedöms motsvara doktorsexamen avlades.
• Erfarenhet av mekanisk provning och röntgenbaserad datortomografi (CT) på mikro- och nanoskala är meriterande
• Goda kunskaper och erfarenheter inom olinjära numeriska analyser med FEM
• Medvetenhet om mångfalds- och likabehandlingsfrågor med särskilt fokus på jämställdhet
• Samarbetsförmåga
• Självständighet

Vi kommer att lägga stor vikt vid personliga egenskaper.

Bli en del av KTH  

KTH formar framtiden genom utbildning, forskning och innovation. Som ett ledande internationellt tekniskt universitet spelar vi en aktiv roll i att driva och medverka i omställningen till ett hållbart samhälle. Här erbjuds du möjligheten att växa och utvecklas på en kreativ och dynamisk arbetsplats med goda arbetsvillkor och förmåner. Jämställdhet, mångfald och lika villkor är en kvalitetsfråga och en självklar del av KTH:s värdegrund som universitet och statlig myndighet.

Läs mer om våra förmåner och hur det är att arbeta och utvecklas på KTH.

Fackliga representanter

Kontaktuppgifter till fackliga representanter.

Ansökan

Du ansöker via KTH:s rekryteringssystem. Du som sökande har huvudansvaret för att din ansökan är komplett när den skickas in.

Ansökan ska innehålla: 

• CV inklusive relevant yrkeserfarenhet och kunskap.
• Kopia av examensbevis och betyg från dina tidigare universitetsstudier. Översättningar till engelska eller svenska om de ursprungliga dokumenten inte utfärdas på något av dessa språk.
• Kortfattad redogörelse för varför du vill bedriva forskning, dina akademiska intressen och hur de relaterar till dina tidigare studier och framtida mål. max 2 sidor lång. 

Ansökan ska vara KTH tillhanda senast sista ansökningsdagen vid midnatt, CET/CEST (Central European Time/Central European Summer Time).

Om anställningen

Anställningen gäller tillsvidare, dock längst två år

En anställning som postdoktor är en tidsbegränsad meriteringsanställning med huvudinriktning mot forskning avsedd som ett första karriärsteg efter disputation.

Övrigt

För information om behandling av personuppgifter i samband med rekrytering.

Det kan förekomma att en anställning hos KTH är placerad i säkerhetsklass. Om så är fallet för just denna anställning görs en säkerhetsprövning av sökande i enlighet med säkerhetsskyddslagen (2018:585) efter samtycke. I dessa fall är en förutsättning för anställning att sökande blir godkänd efter säkerhetsprövning.

Vi undanber oss direktkontakt med bemannings- och rekryteringsföretag samt försäljare av platsannonser.

Arbetsuppgifter

Den utlysta postdoktortjänsten är inom ett ambitiöst, 5-årigt Wallenberg Scholar-projekt med titeln "Scalable and adaptive inferencing for democratizing AI". I detta projekt på 18 miljoner SEK vill vi dramatiskt minska kostnaderna och energiåtgången för att betjäna stora språkmodeller som ChatGPT. Mer information om projektet och vår projektsida med tidiga publikationer.

Arbetet kommer primärt att omfatta design, implementering och utvärdering av distribuerade system och nätverk för maskininlärningsinferens. Tillämpning av maskininlärningskoncept, med målet att utforma agentiska ramverk, kommer också att ingå i projektet.

Som medlem i forskargruppen kommer du att vara involverad i experimentellt systemarbete i samarbete med fakultetsmedlemmar och flera doktorander vid vårt forskningslabb. Du kommer att bidra till vetenskapliga publikationer samt nya bidragsansökningar.

Arbetet kommer utföras i samarbete med Professor Dejan Kostic och Universitetslektor Marco Chiesa.

Kvalifikationer

Krav

• Avlagd doktorsexamen eller utländsk examen som bedöms motsvara en doktorsexamen. Detta behörighetskrav ska vara uppfyllt senast vid tidpunkten då anställningsbeslutet fattas.
• Du ska också ha en gedigen bakgrund inom distribuerade system, nätverk, programmering och operativsystem och mycket goda kunskaper i engelska i tal och skrift då det krävs i dagligt arbete.
• Det är viktigt att du har mycket goda kunskaper i kritiskt tänkande samt mycket goda kunskaper i C++, Python, Linux och i något skriptspråk.
• Vi söker en mycket motiverad kandidat med vilja att experimentera och utforska och som har internationella publikationer i tidskrifter och på topp-konferenser.

Meriterande

• Vid sista ansökningsdag högst tre år sedan doktorsexamen eller en utländsk examen som bedöms motsvara doktorsexamen avlades.
• Pedagogisk förmåga, såväl som medvetenhet om mångfalds- och likabehandlingsfrågor med särskilt fokus på jämställdhet.
• Du har god samarbetsförmåga, men kan även arbeta självständigt.
• Erfarenhet av maskininlärning, GPU-programmering eller storskaliga inferenssystem.
• Medvetenhet om mångfalds- och likabehandlingsfrågor med särskilt fokus på jämställdhet

Vi kommer att lägga stor vikt vid personliga egenskaper.

Bli en del av KTH  

KTH formar framtiden genom utbildning, forskning och innovation. Som ett ledande internationellt tekniskt universitet spelar vi en aktiv roll i att driva och medverka i omställningen till ett hållbart samhälle. Här erbjuds du möjligheten att växa och utvecklas på en kreativ och dynamisk arbetsplats med goda arbetsvillkor och förmåner. Jämställdhet, mångfald och lika villkor är en kvalitetsfråga och en självklar del av KTH:s värdegrund som universitet och statlig myndighet.

Läs mer om våra förmåner och hur det är att arbeta och utvecklas på KTH.

Fackliga representanter

Kontaktuppgifter till fackliga representanter.

Ansökan

Du ansöker via KTH:s rekryteringssystem. Du som sökande har huvudansvaret för att din ansökan är komplett när den skickas in.

Ansökan ska innehålla: 

• CV inklusive relevant yrkeserfarenhet och kunskap.
• Kopia av examensbevis och betyg från dina tidigare universitetsstudier. Översättningar till engelska eller svenska om de ursprungliga dokumenten inte utfärdas på något av dessa språk.
• Kortfattad redogörelse för varför du vill bedriva forskning, dina akademiska intressen och hur de relaterar till dina tidigare studier och framtida mål. max 2 sidor lång. 

Ansökan ska vara KTH tillhanda senast sista ansökningsdagen vid midnatt, CET/CEST (Central European Time/Central European Summer Time).

Om anställningen

Anställningen gäller tillsvidare, dock längst två år

En anställning som postdoktor är en tidsbegränsad meriteringsanställning med huvudinriktning mot forskning avsedd som ett första karriärsteg efter disputation.

Övrigt

För information om behandling av personuppgifter i samband med rekrytering.

Det kan förekomma att en anställning hos KTH är placerad i säkerhetsklass. Om så är fallet för just denna anställning görs en säkerhetsprövning av sökande i enlighet med säkerhetsskyddslagen (2018:585) efter samtycke. I dessa fall är en förutsättning för anställning att sökande blir godkänd efter säkerhetsprövning.

Vi undanber oss direktkontakt med bemannings- och rekryteringsföretag samt försäljare av platsannonser.

AI is developing fast – far beyond the speed of traditional technological evolution, and energy systems are becoming ever more complex, distributed and interconnected. Do you, just like us, want to help build the intelligent, data-driven tools that will keep tomorrow’s energy systems reliable and resilient?

We are looking for a motivated PhD student who wants to develop AI methods and digital twins for resilient energy systems, with a focus on district heating and cooling and building energy systems. You will combine machine learning, modelling and simulation to better understand, predict and strengthen these systems under uncertainty and disturbance – working at the intersection of AI, digital twins and the built environment.

About us

At RISE, the unit Connected Intelligence conducts applied research and development at the meeting point between artificial intelligence, connected systems and the physical world. We build intelligent, data-driven solutions that turn sensor data, models and real-time information into decisions – for industry, public agencies and society.

Our team is interdisciplinary and hands-on. We are a group of researchers who develop practical, trustworthy AI solutions together with industry partners, public agencies and academia. As a PhD student you will be employed at RISE and enrolled as a doctoral student at KTH Royal Institute of Technology, with an academic supervisor at KTH in addition to your supervisors at RISE.

About the role

In this position you will pursue doctoral research on AI and digital twins for resilient energy systems, with a focus on district heating and cooling networks and building energy systems. The overall direction is set, while the specific scientific contributions will be shaped together with you. You will:

• Develop AI and machine-learning methods for modelling, monitoring and forecasting in district heating and cooling networks and building energy systems

• Build and validate digital twins that mirror the behaviour of these energy systems and their assets in real time

• Investigate how data-driven methods can improve the resilience and efficiency of district heating/cooling and building energy systems against faults, disturbances and changing conditions

• Combine physics-based models with data-driven approaches (e.g. hybrid and physics-informed machine learning)

• Validate methods on real data and in relevant testbed or simulation environments together with energy utilities, property owners and research partners

• Publish your results in leading international conferences and journals, and present them in research and industry forums

• Contribute to research and innovation projects within the unit

The position is a full-time, time-limited doctoral employment, normally up to five years including approximately 20% departmental work, leading to a PhD. The role is based in Kista, Stockholm, and you are expected to spend 3 days per week in KTH, Campus Valhallavägen for coursework, research collaboration, and possibly teaching duties.

Because some projects may be security-sensitive, a security clearance may be required now or in the future.

Who are you?

Required qualifications:

• A Master’s degree (or equivalent) in computer science, electrical or energy engineering, applied mathematics, physics or a closely related field

• Solid foundation in machine learning and/or modelling and simulation

• Good programming skills (e.g. Python)

• A strong interest in energy systems – especially district heating/cooling and building energy systems – and in digital twins

• Ability to work independently as well as in a team

• Excellent communication skills in English, written and spoken

Meriting qualifications:

• Experience with deep learning and modern AI frameworks (e.g. PyTorch, TensorFlow)

• Strong knowledge of energy systems, especially district heating/cooling and building energy systems, combined with strong modelling and simulation skills

• Experience with digital twins, simulation or physics-informed/hybrid modelling

• Experience working with time-series data, sensor data or real-time systems

• Experience with optimisation, control or uncertainty quantification

• Prior research experience or scientific publications

• Good communication skills in Swedish

Personal qualities:

• A strong technical interest and a desire to work at the forefront of technology

• Curiosity and a drive to learn, explore and solve complex problems

• Strong analytical skills

• Communicative and able to collaborate with both technical and non-technical stakeholders

• Proactive, with the ability to take initiative and see the bigger picture in complex systems

Are we a good match?

We work across the entire AI pipeline – from data collection and communication to modelling, learning and decision-making – with a focus on trustworthiness, robustness and real-world impact. Resilient and efficient district heating/cooling and building energy systems are a strategic societal challenge for the energy transition, and digital twins powered by AI are one of the most promising tools to address it.

As a PhD student at RISE you will have:

• The opportunity to do impactful research on a strategically important societal challenge

• Access to real data, testbeds and simulation environments together with leading partners

• Close collaboration with experienced researchers and industry partners

• A combination of applied research and academic training, leading to a PhD

• A flexible, supportive and research-driven work environment

AI is developing fast – far beyond the speed of traditional technological evolution, and energy systems are becoming ever more complex, distributed and interconnected. Do you, just like us, want to help build the intelligent, data-driven tools that will keep tomorrow’s energy systems reliable and resilient?

We are looking for a motivated PhD student who wants to develop AI methods and digital twins for resilient energy systems, with a focus on district heating and cooling and building energy systems. You will combine machine learning, modelling and simulation to better understand, predict and strengthen these systems under uncertainty and disturbance – working at the intersection of AI, digital twins and the built environment.

About us

At RISE, the unit Connected Intelligence conducts applied research and development at the meeting point between artificial intelligence, connected systems and the physical world. We build intelligent, data-driven solutions that turn sensor data, models and real-time information into decisions – for industry, public agencies and society.

Our team is interdisciplinary and hands-on. We are a group of researchers who develop practical, trustworthy AI solutions together with industry partners, public agencies and academia. As a PhD student you will be employed at RISE and enrolled as a doctoral student at KTH Royal Institute of Technology, with an academic supervisor at KTH in addition to your supervisors at RISE.

About the role

In this position you will pursue doctoral research on AI and digital twins for resilient energy systems, with a focus on district heating and cooling networks and building energy systems. The overall direction is set, while the specific scientific contributions will be shaped together with you. You will:

• Develop AI and machine-learning methods for modelling, monitoring and forecasting in district heating and cooling networks and building energy systems

• Build and validate digital twins that mirror the behaviour of these energy systems and their assets in real time

• Investigate how data-driven methods can improve the resilience and efficiency of district heating/cooling and building energy systems against faults, disturbances and changing conditions

• Combine physics-based models with data-driven approaches (e.g. hybrid and physics-informed machine learning)

• Validate methods on real data and in relevant testbed or simulation environments together with energy utilities, property owners and research partners

• Publish your results in leading international conferences and journals, and present them in research and industry forums

• Contribute to research and innovation projects within the unit

The position is a full-time, time-limited doctoral employment, normally up to five years including approximately 20% departmental work, leading to a PhD. The role is based in Kista, Stockholm, and you are expected to spend 3 days per week in KTH, Campus Valhallavägen for coursework, research collaboration, and possibly teaching duties.

Because some projects may be security-sensitive, a security clearance may be required now or in the future.

Who are you?

Required qualifications:

• A Master’s degree (or equivalent) in computer science, electrical or energy engineering, applied mathematics, physics or a closely related field

• Solid foundation in machine learning and/or modelling and simulation

• Good programming skills (e.g. Python)

• A strong interest in energy systems – especially district heating/cooling and building energy systems – and in digital twins

• Ability to work independently as well as in a team

• Excellent communication skills in English, written and spoken

Meriting qualifications:

• Experience with deep learning and modern AI frameworks (e.g. PyTorch, TensorFlow)

• Strong knowledge of energy systems, especially district heating/cooling and building energy systems, combined with strong modelling and simulation skills

• Experience with digital twins, simulation or physics-informed/hybrid modelling

• Experience working with time-series data, sensor data or real-time systems

• Experience with optimisation, control or uncertainty quantification

• Prior research experience or scientific publications

• Good communication skills in Swedish

Personal qualities:

• A strong technical interest and a desire to work at the forefront of technology

• Curiosity and a drive to learn, explore and solve complex problems

• Strong analytical skills

• Communicative and able to collaborate with both technical and non-technical stakeholders

• Proactive, with the ability to take initiative and see the bigger picture in complex systems

Are we a good match?

We work across the entire AI pipeline – from data collection and communication to modelling, learning and decision-making – with a focus on trustworthiness, robustness and real-world impact. Resilient and efficient district heating/cooling and building energy systems are a strategic societal challenge for the energy transition, and digital twins powered by AI are one of the most promising tools to address it.

As a PhD student at RISE you will have:

• The opportunity to do impactful research on a strategically important societal challenge

• Access to real data, testbeds and simulation environments together with leading partners

• Close collaboration with experienced researchers and industry partners

• A combination of applied research and academic training, leading to a PhD

• A flexible, supportive and research-driven work environment

AI is developing fast – far beyond the speed of traditional technological evolution, and energy systems are becoming ever more complex, distributed and interconnected. Do you, just like us, want to help build the intelligent, data-driven tools that will keep tomorrow’s energy systems reliable and resilient?

We are looking for a motivated PhD student who wants to develop AI methods and digital twins for resilient energy systems, with a focus on district heating and cooling and building energy systems. You will combine machine learning, modelling and simulation to better understand, predict and strengthen these systems under uncertainty and disturbance – working at the intersection of AI, digital twins and the built environment.

About us

At RISE, the unit Connected Intelligence conducts applied research and development at the meeting point between artificial intelligence, connected systems and the physical world. We build intelligent, data-driven solutions that turn sensor data, models and real-time information into decisions – for industry, public agencies and society.

Our team is interdisciplinary and hands-on. We are a group of researchers who develop practical, trustworthy AI solutions together with industry partners, public agencies and academia. As a PhD student you will be employed at RISE and enrolled as a doctoral student at KTH Royal Institute of Technology, with an academic supervisor at KTH in addition to your supervisors at RISE.

About the role

In this position you will pursue doctoral research on AI and digital twins for resilient energy systems, with a focus on district heating and cooling networks and building energy systems. The overall direction is set, while the specific scientific contributions will be shaped together with you. You will:

• Develop AI and machine-learning methods for modelling, monitoring and forecasting in district heating and cooling networks and building energy systems

• Build and validate digital twins that mirror the behaviour of these energy systems and their assets in real time

• Investigate how data-driven methods can improve the resilience and efficiency of district heating/cooling and building energy systems against faults, disturbances and changing conditions

• Combine physics-based models with data-driven approaches (e.g. hybrid and physics-informed machine learning)

• Validate methods on real data and in relevant testbed or simulation environments together with energy utilities, property owners and research partners

• Publish your results in leading international conferences and journals, and present them in research and industry forums

• Contribute to research and innovation projects within the unit

The position is a full-time, time-limited doctoral employment, normally up to five years including approximately 20% departmental work, leading to a PhD. The role is based in Kista, Stockholm, and you are expected to spend 3 days per week in KTH, Campus Valhallavägen for coursework, research collaboration, and possibly teaching duties.

Because some projects may be security-sensitive, a security clearance may be required now or in the future.

Who are you?

Required qualifications:

• A Master’s degree (or equivalent) in computer science, electrical or energy engineering, applied mathematics, physics or a closely related field

• Solid foundation in machine learning and/or modelling and simulation

• Good programming skills (e.g. Python)

• A strong interest in energy systems – especially district heating/cooling and building energy systems – and in digital twins

• Ability to work independently as well as in a team

• Excellent communication skills in English, written and spoken

Meriting qualifications:

• Experience with deep learning and modern AI frameworks (e.g. PyTorch, TensorFlow)

• Strong knowledge of energy systems, especially district heating/cooling and building energy systems, combined with strong modelling and simulation skills

• Experience with digital twins, simulation or physics-informed/hybrid modelling

• Experience working with time-series data, sensor data or real-time systems

• Experience with optimisation, control or uncertainty quantification

• Prior research experience or scientific publications

• Good communication skills in Swedish

Personal qualities:

• A strong technical interest and a desire to work at the forefront of technology

• Curiosity and a drive to learn, explore and solve complex problems

• Strong analytical skills

• Communicative and able to collaborate with both technical and non-technical stakeholders

• Proactive, with the ability to take initiative and see the bigger picture in complex systems

Are we a good match?

We work across the entire AI pipeline – from data collection and communication to modelling, learning and decision-making – with a focus on trustworthiness, robustness and real-world impact. Resilient and efficient district heating/cooling and building energy systems are a strategic societal challenge for the energy transition, and digital twins powered by AI are one of the most promising tools to address it.

As a PhD student at RISE you will have:

• The opportunity to do impactful research on a strategically important societal challenge

• Access to real data, testbeds and simulation environments together with leading partners

• Close collaboration with experienced researchers and industry partners

• A combination of applied research and academic training, leading to a PhD

• A flexible, supportive and research-driven work environment

AI is developing fast – far beyond the speed of traditional technological evolution, and energy systems are becoming ever more complex, distributed and interconnected. Do you, just like us, want to help build the intelligent, data-driven tools that will keep tomorrow’s energy systems reliable and resilient?

We are looking for a motivated PhD student who wants to develop AI methods and digital twins for resilient energy systems, with a focus on district heating and cooling and building energy systems. You will combine machine learning, modelling and simulation to better understand, predict and strengthen these systems under uncertainty and disturbance – working at the intersection of AI, digital twins and the built environment.

About us

At RISE, the unit Connected Intelligence conducts applied research and development at the meeting point between artificial intelligence, connected systems and the physical world. We build intelligent, data-driven solutions that turn sensor data, models and real-time information into decisions – for industry, public agencies and society.

Our team is interdisciplinary and hands-on. We are a group of researchers who develop practical, trustworthy AI solutions together with industry partners, public agencies and academia. As a PhD student you will be employed at RISE and enrolled as a doctoral student at KTH Royal Institute of Technology, with an academic supervisor at KTH in addition to your supervisors at RISE.

About the role

In this position you will pursue doctoral research on AI and digital twins for resilient energy systems, with a focus on district heating and cooling networks and building energy systems. The overall direction is set, while the specific scientific contributions will be shaped together with you. You will:

• Develop AI and machine-learning methods for modelling, monitoring and forecasting in district heating and cooling networks and building energy systems

• Build and validate digital twins that mirror the behaviour of these energy systems and their assets in real time

• Investigate how data-driven methods can improve the resilience and efficiency of district heating/cooling and building energy systems against faults, disturbances and changing conditions

• Combine physics-based models with data-driven approaches (e.g. hybrid and physics-informed machine learning)

• Validate methods on real data and in relevant testbed or simulation environments together with energy utilities, property owners and research partners

• Publish your results in leading international conferences and journals, and present them in research and industry forums

• Contribute to research and innovation projects within the unit

The position is a full-time, time-limited doctoral employment, normally up to five years including approximately 20% departmental work, leading to a PhD. The role is based in Kista, Stockholm, and you are expected to spend 3 days per week in KTH, Campus Valhallavägen for coursework, research collaboration, and possibly teaching duties.

Because some projects may be security-sensitive, a security clearance may be required now or in the future.

Who are you?

Required qualifications:

• A Master’s degree (or equivalent) in computer science, electrical or energy engineering, applied mathematics, physics or a closely related field

• Solid foundation in machine learning and/or modelling and simulation

• Good programming skills (e.g. Python)

• A strong interest in energy systems – especially district heating/cooling and building energy systems – and in digital twins

• Ability to work independently as well as in a team

• Excellent communication skills in English, written and spoken

Meriting qualifications:

• Experience with deep learning and modern AI frameworks (e.g. PyTorch, TensorFlow)

• Strong knowledge of energy systems, especially district heating/cooling and building energy systems, combined with strong modelling and simulation skills

• Experience with digital twins, simulation or physics-informed/hybrid modelling

• Experience working with time-series data, sensor data or real-time systems

• Experience with optimisation, control or uncertainty quantification

• Prior research experience or scientific publications

• Good communication skills in Swedish

Personal qualities:

• A strong technical interest and a desire to work at the forefront of technology

• Curiosity and a drive to learn, explore and solve complex problems

• Strong analytical skills

• Communicative and able to collaborate with both technical and non-technical stakeholders

• Proactive, with the ability to take initiative and see the bigger picture in complex systems

Are we a good match?

We work across the entire AI pipeline – from data collection and communication to modelling, learning and decision-making – with a focus on trustworthiness, robustness and real-world impact. Resilient and efficient district heating/cooling and building energy systems are a strategic societal challenge for the energy transition, and digital twins powered by AI are one of the most promising tools to address it.

As a PhD student at RISE you will have:

• The opportunity to do impactful research on a strategically important societal challenge

• Access to real data, testbeds and simulation environments together with leading partners

• Close collaboration with experienced researchers and industry partners

• A combination of applied research and academic training, leading to a PhD

• A flexible, supportive and research-driven work environment

AI is developing fast – far beyond the speed of traditional technological evolution, and energy systems are becoming ever more complex, distributed and interconnected. Do you, just like us, want to help build the intelligent, data-driven tools that will keep tomorrow’s energy systems reliable and resilient?

We are looking for a motivated PhD student who wants to develop AI methods and digital twins for resilient energy systems, with a focus on district heating and cooling and building energy systems. You will combine machine learning, modelling and simulation to better understand, predict and strengthen these systems under uncertainty and disturbance – working at the intersection of AI, digital twins and the built environment.

About us

At RISE, the unit Connected Intelligence conducts applied research and development at the meeting point between artificial intelligence, connected systems and the physical world. We build intelligent, data-driven solutions that turn sensor data, models and real-time information into decisions – for industry, public agencies and society.

Our team is interdisciplinary and hands-on. We are a group of researchers who develop practical, trustworthy AI solutions together with industry partners, public agencies and academia. As a PhD student you will be employed at RISE and enrolled as a doctoral student at KTH Royal Institute of Technology, with an academic supervisor at KTH in addition to your supervisors at RISE.

About the role

In this position you will pursue doctoral research on AI and digital twins for resilient energy systems, with a focus on district heating and cooling networks and building energy systems. The overall direction is set, while the specific scientific contributions will be shaped together with you. You will:

• Develop AI and machine-learning methods for modelling, monitoring and forecasting in district heating and cooling networks and building energy systems

• Build and validate digital twins that mirror the behaviour of these energy systems and their assets in real time

• Investigate how data-driven methods can improve the resilience and efficiency of district heating/cooling and building energy systems against faults, disturbances and changing conditions

• Combine physics-based models with data-driven approaches (e.g. hybrid and physics-informed machine learning)

• Validate methods on real data and in relevant testbed or simulation environments together with energy utilities, property owners and research partners

• Publish your results in leading international conferences and journals, and present them in research and industry forums

• Contribute to research and innovation projects within the unit

The position is a full-time, time-limited doctoral employment, normally up to five years including approximately 20% departmental work, leading to a PhD. The role is based in Kista, Stockholm, and you are expected to spend 3 days per week in KTH, Campus Valhallavägen for coursework, research collaboration, and possibly teaching duties.

Because some projects may be security-sensitive, a security clearance may be required now or in the future.

Who are you?

Required qualifications:

• A Master’s degree (or equivalent) in computer science, electrical or energy engineering, applied mathematics, physics or a closely related field

• Solid foundation in machine learning and/or modelling and simulation

• Good programming skills (e.g. Python)

• A strong interest in energy systems – especially district heating/cooling and building energy systems – and in digital twins

• Ability to work independently as well as in a team

• Excellent communication skills in English, written and spoken

Meriting qualifications:

• Experience with deep learning and modern AI frameworks (e.g. PyTorch, TensorFlow)

• Strong knowledge of energy systems, especially district heating/cooling and building energy systems, combined with strong modelling and simulation skills

• Experience with digital twins, simulation or physics-informed/hybrid modelling

• Experience working with time-series data, sensor data or real-time systems

• Experience with optimisation, control or uncertainty quantification

• Prior research experience or scientific publications

• Good communication skills in Swedish

Personal qualities:

• A strong technical interest and a desire to work at the forefront of technology

• Curiosity and a drive to learn, explore and solve complex problems

• Strong analytical skills

• Communicative and able to collaborate with both technical and non-technical stakeholders

• Proactive, with the ability to take initiative and see the bigger picture in complex systems

Are we a good match?

We work across the entire AI pipeline – from data collection and communication to modelling, learning and decision-making – with a focus on trustworthiness, robustness and real-world impact. Resilient and efficient district heating/cooling and building energy systems are a strategic societal challenge for the energy transition, and digital twins powered by AI are one of the most promising tools to address it.

As a PhD student at RISE you will have:

• The opportunity to do impactful research on a strategically important societal challenge

• Access to real data, testbeds and simulation environments together with leading partners

• Close collaboration with experienced researchers and industry partners

• A combination of applied research and academic training, leading to a PhD

• A flexible, supportive and research-driven work environment

AI is developing fast – far beyond the speed of traditional technological evolution, and energy systems are becoming ever more complex, distributed and interconnected. Do you, just like us, want to help build the intelligent, data-driven tools that will keep tomorrow’s energy systems reliable and resilient?

We are looking for a motivated PhD student who wants to develop AI methods and digital twins for resilient energy systems, with a focus on district heating and cooling and building energy systems. You will combine machine learning, modelling and simulation to better understand, predict and strengthen these systems under uncertainty and disturbance – working at the intersection of AI, digital twins and the built environment.

About us

At RISE, the unit Connected Intelligence conducts applied research and development at the meeting point between artificial intelligence, connected systems and the physical world. We build intelligent, data-driven solutions that turn sensor data, models and real-time information into decisions – for industry, public agencies and society.

Our team is interdisciplinary and hands-on. We are a group of researchers who develop practical, trustworthy AI solutions together with industry partners, public agencies and academia. As a PhD student you will be employed at RISE and enrolled as a doctoral student at KTH Royal Institute of Technology, with an academic supervisor at KTH in addition to your supervisors at RISE.

About the role

In this position you will pursue doctoral research on AI and digital twins for resilient energy systems, with a focus on district heating and cooling networks and building energy systems. The overall direction is set, while the specific scientific contributions will be shaped together with you. You will:

• Develop AI and machine-learning methods for modelling, monitoring and forecasting in district heating and cooling networks and building energy systems

• Build and validate digital twins that mirror the behaviour of these energy systems and their assets in real time

• Investigate how data-driven methods can improve the resilience and efficiency of district heating/cooling and building energy systems against faults, disturbances and changing conditions

• Combine physics-based models with data-driven approaches (e.g. hybrid and physics-informed machine learning)

• Validate methods on real data and in relevant testbed or simulation environments together with energy utilities, property owners and research partners

• Publish your results in leading international conferences and journals, and present them in research and industry forums

• Contribute to research and innovation projects within the unit

The position is a full-time, time-limited doctoral employment, normally up to five years including approximately 20% departmental work, leading to a PhD. The role is based in Kista, Stockholm, and you are expected to spend 3 days per week in KTH, Campus Valhallavägen for coursework, research collaboration, and possibly teaching duties.

Because some projects may be security-sensitive, a security clearance may be required now or in the future.

Who are you?

Required qualifications:

• A Master’s degree (or equivalent) in computer science, electrical or energy engineering, applied mathematics, physics or a closely related field

• Solid foundation in machine learning and/or modelling and simulation

• Good programming skills (e.g. Python)

• A strong interest in energy systems – especially district heating/cooling and building energy systems – and in digital twins

• Ability to work independently as well as in a team

• Excellent communication skills in English, written and spoken

Meriting qualifications:

• Experience with deep learning and modern AI frameworks (e.g. PyTorch, TensorFlow)

• Strong knowledge of energy systems, especially district heating/cooling and building energy systems, combined with strong modelling and simulation skills

• Experience with digital twins, simulation or physics-informed/hybrid modelling

• Experience working with time-series data, sensor data or real-time systems

• Experience with optimisation, control or uncertainty quantification

• Prior research experience or scientific publications

• Good communication skills in Swedish

Personal qualities:

• A strong technical interest and a desire to work at the forefront of technology

• Curiosity and a drive to learn, explore and solve complex problems

• Strong analytical skills

• Communicative and able to collaborate with both technical and non-technical stakeholders

• Proactive, with the ability to take initiative and see the bigger picture in complex systems

Are we a good match?

We work across the entire AI pipeline – from data collection and communication to modelling, learning and decision-making – with a focus on trustworthiness, robustness and real-world impact. Resilient and efficient district heating/cooling and building energy systems are a strategic societal challenge for the energy transition, and digital twins powered by AI are one of the most promising tools to address it.

As a PhD student at RISE you will have:

• The opportunity to do impactful research on a strategically important societal challenge

• Access to real data, testbeds and simulation environments together with leading partners

• Close collaboration with experienced researchers and industry partners

• A combination of applied research and academic training, leading to a PhD

• A flexible, supportive and research-driven work environment

AI is developing fast – far beyond the speed of traditional technological evolution, and energy systems are becoming ever more complex, distributed and interconnected. Do you, just like us, want to help build the intelligent, data-driven tools that will keep tomorrow’s energy systems reliable and resilient?

We are looking for a motivated PhD student who wants to develop AI methods and digital twins for resilient energy systems, with a focus on district heating and cooling and building energy systems. You will combine machine learning, modelling and simulation to better understand, predict and strengthen these systems under uncertainty and disturbance – working at the intersection of AI, digital twins and the built environment.

About us

At RISE, the unit Connected Intelligence conducts applied research and development at the meeting point between artificial intelligence, connected systems and the physical world. We build intelligent, data-driven solutions that turn sensor data, models and real-time information into decisions – for industry, public agencies and society.

Our team is interdisciplinary and hands-on. We are a group of researchers who develop practical, trustworthy AI solutions together with industry partners, public agencies and academia. As a PhD student you will be employed at RISE and enrolled as a doctoral student at KTH Royal Institute of Technology, with an academic supervisor at KTH in addition to your supervisors at RISE.

About the role

In this position you will pursue doctoral research on AI and digital twins for resilient energy systems, with a focus on district heating and cooling networks and building energy systems. The overall direction is set, while the specific scientific contributions will be shaped together with you. You will:

• Develop AI and machine-learning methods for modelling, monitoring and forecasting in district heating and cooling networks and building energy systems

• Build and validate digital twins that mirror the behaviour of these energy systems and their assets in real time

• Investigate how data-driven methods can improve the resilience and efficiency of district heating/cooling and building energy systems against faults, disturbances and changing conditions

• Combine physics-based models with data-driven approaches (e.g. hybrid and physics-informed machine learning)

• Validate methods on real data and in relevant testbed or simulation environments together with energy utilities, property owners and research partners

• Publish your results in leading international conferences and journals, and present them in research and industry forums

• Contribute to research and innovation projects within the unit

The position is a full-time, time-limited doctoral employment, normally up to five years including approximately 20% departmental work, leading to a PhD. The role is based in Kista, Stockholm, and you are expected to spend 3 days per week in KTH, Campus Valhallavägen for coursework, research collaboration, and possibly teaching duties.

Because some projects may be security-sensitive, a security clearance may be required now or in the future.

Who are you?

Required qualifications:

• A Master’s degree (or equivalent) in computer science, electrical or energy engineering, applied mathematics, physics or a closely related field

• Solid foundation in machine learning and/or modelling and simulation

• Good programming skills (e.g. Python)

• A strong interest in energy systems – especially district heating/cooling and building energy systems – and in digital twins

• Ability to work independently as well as in a team

• Excellent communication skills in English, written and spoken

Meriting qualifications:

• Experience with deep learning and modern AI frameworks (e.g. PyTorch, TensorFlow)

• Strong knowledge of energy systems, especially district heating/cooling and building energy systems, combined with strong modelling and simulation skills

• Experience with digital twins, simulation or physics-informed/hybrid modelling

• Experience working with time-series data, sensor data or real-time systems

• Experience with optimisation, control or uncertainty quantification

• Prior research experience or scientific publications

• Good communication skills in Swedish

Personal qualities:

• A strong technical interest and a desire to work at the forefront of technology

• Curiosity and a drive to learn, explore and solve complex problems

• Strong analytical skills

• Communicative and able to collaborate with both technical and non-technical stakeholders

• Proactive, with the ability to take initiative and see the bigger picture in complex systems

Are we a good match?

We work across the entire AI pipeline – from data collection and communication to modelling, learning and decision-making – with a focus on trustworthiness, robustness and real-world impact. Resilient and efficient district heating/cooling and building energy systems are a strategic societal challenge for the energy transition, and digital twins powered by AI are one of the most promising tools to address it.

As a PhD student at RISE you will have:

• The opportunity to do impactful research on a strategically important societal challenge

• Access to real data, testbeds and simulation environments together with leading partners

• Close collaboration with experienced researchers and industry partners

• A combination of applied research and academic training, leading to a PhD

• A flexible, supportive and research-driven work environment

Elektrifiering och digitalisering tillhör framtidens största områden för omställning till hållbara samhällen. Institutionen för elektroteknik bedriver framgångsrik forskning och utbildning inom dessa områden – däribland förnybara energikällor och elfordon, industriell IoT, 6G kommunikation och trådlösa sensornät men även forskning och utbildning inom Life Science, hälsoteknik, smarta elektroniska sensorer och medicinska system. Institutionen för elektroteknik är en internationell miljö med ca 160 medarbetare som bidrar till viktiga tekniska energi- och hälsolösningar vid Ångströmlaboratoriet.

Vi söker nu en doktorand inom Cybersäkerhet för elsystem.

Kom och jobba med oss!

Tjänsten är placerad vid avdelningen för elektricitetslära, inom Institutionen för elektroteknik. Här hittar du en trevlig arbetsmiljö med ett aktivt doktorandnätverk och en mängd experimentella projekt. Avdelningen samarbetar med svenska företag – både offentliga och privata – samt andra intressenter inom de olika forskningsområdena. Vi ser fram emot din ansökan. Var med och bygg framtiden med oss!

Läs mer om våra förmåner och hur det är att jobba inom Uppsala universitet.

Projektbeskrivning

Cyberattacker och cyberkrigsföring är ett ökande hot mot all samhällskritisk infrastruktur. Elsystemet är särskilt utsatt eftersom annan kritisk infrastruktur, såsom sjukvård, kommunikation och betalsystem, är beroende av det.

Vi söker en doktorand som vill bedriva viktig och utmanande forskning inom området cybersäkerhet för elsystem. Med hjälp av bland annat artificiell intelligens (AI)/ maskininlärning (ML), hotmodellering och simuleringar kan vi lära oss mer om hur cybersäkerheten kan förbättras. 

Följande är exempel på möjliga projekt:

• Cybersäkerhet för elkraftnätens stationer och deras datakommunikation
• Cyberhot mot framtidens elsystem
• Cyberresiliens i elsystem
• Cybersäkra elsystem med hjälp av AI/ML

Vi erbjuder ett varierande och spännande arbete som utformas av doktoranderna och forskargruppen tillsammans. Varje doktorand kommer att handledas av minst två handledare. Institutionen för elektroteknik erbjuder även lönetillägg vid anställning som doktorand vid institutionen.

Arbetsuppgifter

• Forska kring ämnet cybersäkerhet för elsystem med syfte att producera vetenskapliga artiklar.
• Utveckla och arbeta med mjukvaruverktyg som hjälper till att bedriva forskning för att till exempel simulera dataflöden eller cyberattacker.

Undervisning och övrig institutionstjänstgöring kommer även att ingå i arbetsuppgifterna, dock max 20% av arbetstiden. 

Kvalifikationskrav

Behörig till utbildning på forskarnivå är den som har

• avlagt examen på avancerad nivå, såsom civilingenjörsexamen eller motsvarande utländsk examen som bedöms motsvara masterexamen, inom datavetenskap, elektroteknik, energisystem, informationsteknologi, system i teknik och samhälle, teknisk fysik eller motsvarande, eller
• fullgjort minst 240 högskolepoäng varav minst 60 högskolepoäng på avancerad nivå inklusive ett självständigt arbete om minst 15 högskolepoäng, eller
• på något annat sätt förvärvat i huvudsak motsvarande kunskaper.

Vi lägger stor vikt vid dina personliga egenskaper. Du har god samarbets- och kommunikationsförmåga. Du är målmedveten, strukturerad och noggrann, och kan arbeta effektivt både självständigt och tillsammans med andra. Du tar ansvar för ditt arbete och driver det framåt.  Eftersom tjänsten innebär viss undervisning behöver du också ha intresse för undervisning och kunna förklara på ett tydligt sätt.

För tjänsten krävs att du har god förmåga att uttrycka dig på svenska och engelska i både tal och skrift.

Meriterande

Intresse för och kunskap inom cybersäkerhet, datakommunikation och/eller elsystem.

Om anställningen

Tidsbegränsad anställning i 48 månader, enligt HF 5 kap § 7. Omfattningen är heltid. Tillträde 2026-09-01 eller senare, enligt överenskommelse.  Placeringsort Uppsala.

Bestämmelser för doktorander återfinns i Högskoleförordningen 5 kap §§ 1-7 samt i universitetets regler och riktlinjer.

Upplysningar om anställningen lämnas av

Professor Göran Ericsson, goran.n.ericsson@uu.se +46 18 471 58 22.

Instruktioner för ansökan

Ansökan ska innehålla 1) ett brev som beskriver dig själv, dina forskningsintressen och varför du vill doktorera, 2) ditt CV, 3) en kort beskrivning av din utbildning, 4) en kopia av din masterexamen och dina kursbetyg samt en kopia av din masteruppsats 5) eventuellt andra handlingar som du önskar åberopa, 6) namn och kontaktuppgifter till referenspersoner (e-postadress och telefonnummer), och 7) eventuella publikationer. Ansökan ska skrivas på svenska.

Välkommen med din ansökan senast den 10 augusti 2026. UFV-PA 2026/1999.

Uppsala universitet är ett brett forskningsuniversitet med stark internationell ställning. Det yttersta målet är att bedriva utbildning och forskning av högsta kvalitet och relevans för att göra skillnad i samhället. Vår viktigaste tillgång är alla 7 500 anställda och 53 000 studenter som med nyfikenhet och engagemang gör Uppsala universitet till en av landets mest spännande arbetsplatser.

Läs mer om våra förmåner och hur det är att jobba inom Uppsala universitet

https://uu.se/om-uu/jobba-hos-oss/

Anställningen kan komma att säkerhetsprövas. Vid säkerhetsprövning är en förutsättning för anställning att sökande blir godkänd.

Vi undanber oss erbjudanden om rekryterings- och annonseringshjälp.

Ansökan tas emot i Uppsala universitets rekryteringssystem.

Fackliga företrädare: Saco-S - saco-s@uu.se, Seko - seko@uadm.uu.se, ST (OFR/S) - ofr@uu.se