Lediga job som Ingenjör inom data/elektronik

Silex Microsystems is the world’s leading pure-play MEMS foundry. We customize MEMS solutions for prominent global clients in industries such as pharmaceuticals, consumer electronics, and the automotive industry. We were founded in 2000 in a KTH lab by, among others, current CEO Edvard Kälvesten. Our headquarters and factory are located in Järfälla.

About the role

We are looking for an experienced and structured Product Manager to play a key role in ensuring that Silex meets its On-Time Delivery goals while driving product- and production-related activities forward.

In this role, you will combine project and program management, production planning, and customer management. You will be the primary interface between production, customers, and external partners, taking responsibility for planning and coordinating products from order through delivery.

You will also drive and lead projects and improvement initiatives related to products, production processes, planning, and systems. As Product Owner, you will ensure that product-related requirements, priorities, data, and systems are aligned and continuously improved.

The role is highly cross-functional and requires someone with a strong technical interest, excellent communication skills, and the ability to create structure and drive activities forward in a complex production environment.

Key Responsibilities

• Act as the primary point of contact for customers and external partners regarding production-related matters, including delivery schedules, plans, status updates, and technical inquiries.

• Drive and lead projects and programs related to product, production, planning, and continuous improvement.

• Take product ownership, ensuring that product requirements, priorities, processes, data, and systems are clearly defined and maintained.

• Plan and manage products from order to delivery, ensuring efficient production flow and on-time delivery.

• Follow up on status and movement throughout the production cycle and proactively identify and resolve deviations.

• Monitor and control product starts, ensuring timely execution and delivery.

• Manage Work-In-Progress (WIP) updates and track production flow through the fab.

• Be responsible for Product Quality and ensure compliance with our Customer Product Integration (CPI) process.

• Coordinate activities across production, engineering, quality, planning, and other internal functions.

• Drive product improvements and development initiatives together with internal teams, customers, and partners.

• Support the development and implementation of new WIP management and planning tools.

• Ensure clear communication, documentation, follow-up, and structure throughout projects and product activities.

We are looking for someone who

We believe you have several years of experience driving and leading projects, preferably in a technical, manufacturing, or process-industry environment. You are comfortable taking ownership, coordinating multiple stakeholders, and driving activities from idea to implementation.

You have:

• An engineering Master’s degree or equivalent within materials, nanotechnology, physics, chemical engineering, or a similar field.

• Experience in project management and the ability to independently drive projects and programs to completion.

• Experience and knowledge of MEMS, semiconductor technology, or a closely related field is considered highly beneficial.

• Experience in production planning, product management, or technical project management.

• Experience of working with and managing customers and/or external partners.

• A strong technical interest and the ability to understand and communicate complex technical topics.

• Excellent communication skills and the ability to build strong relationships across different functions and organizational levels.

• A highly structured and organized way of working, with strong attention to detail and the ability to prioritize in a complex environment.

• A collaborative and solution-oriented mindset, combined with the confidence to take initiative and make decisions.

• Fluency in English; Swedish is highly meritorious.

Above all, we are looking for someone who enjoys working at the intersection of technology, production, customers, and business, and who is motivated by taking ownership and making things happen.

More about the recruitment process

We apply an ongoing recruitment process and therefore encourage you to submit your application as soon as possible. To apply, please attach your CV and answer a few questions.

In the process, we work with a competence-based approach, which will include a personality and logic test, as well as reference checks. We will also conduct both a background check and a drug test before employment. As an employer, we also carry out random alcohol and drug tests on all employees.

Silex Microsystems is the world’s leading pure-play MEMS foundry. We customize MEMS solutions for prominent global clients in industries such as pharmaceuticals, consumer electronics, and the automotive industry. We were founded in 2000 in a KTH lab by, among others, current CEO Edvard Kälvesten. Our headquarters and factory are located in Järfälla.

About the role

We are looking for an experienced and structured Product Manager to play a key role in ensuring that Silex meets its On-Time Delivery goals while driving product- and production-related activities forward.

In this role, you will combine project and program management, production planning, and customer management. You will be the primary interface between production, customers, and external partners, taking responsibility for planning and coordinating products from order through delivery.

You will also drive and lead projects and improvement initiatives related to products, production processes, planning, and systems. As Product Owner, you will ensure that product-related requirements, priorities, data, and systems are aligned and continuously improved.

The role is highly cross-functional and requires someone with a strong technical interest, excellent communication skills, and the ability to create structure and drive activities forward in a complex production environment.

Key Responsibilities

• Act as the primary point of contact for customers and external partners regarding production-related matters, including delivery schedules, plans, status updates, and technical inquiries.

• Drive and lead projects and programs related to product, production, planning, and continuous improvement.

• Take product ownership, ensuring that product requirements, priorities, processes, data, and systems are clearly defined and maintained.

• Plan and manage products from order to delivery, ensuring efficient production flow and on-time delivery.

• Follow up on status and movement throughout the production cycle and proactively identify and resolve deviations.

• Monitor and control product starts, ensuring timely execution and delivery.

• Manage Work-In-Progress (WIP) updates and track production flow through the fab.

• Be responsible for Product Quality and ensure compliance with our Customer Product Integration (CPI) process.

• Coordinate activities across production, engineering, quality, planning, and other internal functions.

• Drive product improvements and development initiatives together with internal teams, customers, and partners.

• Support the development and implementation of new WIP management and planning tools.

• Ensure clear communication, documentation, follow-up, and structure throughout projects and product activities.

We are looking for someone who

We believe you have several years of experience driving and leading projects, preferably in a technical, manufacturing, or process-industry environment. You are comfortable taking ownership, coordinating multiple stakeholders, and driving activities from idea to implementation.

You have:

• An engineering Master’s degree or equivalent within materials, nanotechnology, physics, chemical engineering, or a similar field.

• Experience in project management and the ability to independently drive projects and programs to completion.

• Experience and knowledge of MEMS, semiconductor technology, or a closely related field is considered highly beneficial.

• Experience in production planning, product management, or technical project management.

• Experience of working with and managing customers and/or external partners.

• A strong technical interest and the ability to understand and communicate complex technical topics.

• Excellent communication skills and the ability to build strong relationships across different functions and organizational levels.

• A highly structured and organized way of working, with strong attention to detail and the ability to prioritize in a complex environment.

• A collaborative and solution-oriented mindset, combined with the confidence to take initiative and make decisions.

• Fluency in English; Swedish is highly meritorious.

Above all, we are looking for someone who enjoys working at the intersection of technology, production, customers, and business, and who is motivated by taking ownership and making things happen.

More about the recruitment process

We apply an ongoing recruitment process and therefore encourage you to submit your application as soon as possible. To apply, please attach your CV and answer a few questions.

In the process, we work with a competence-based approach, which will include a personality and logic test, as well as reference checks. We will also conduct both a background check and a drug test before employment. As an employer, we also carry out random alcohol and drug tests on all employees.

Silex Microsystems is the world’s leading pure-play MEMS foundry. We customize MEMS solutions for prominent global clients in industries such as pharmaceuticals, consumer electronics, and the automotive industry. We were founded in 2000 in a KTH lab by, among others, current CEO Edvard Kälvesten. Our headquarters and factory are located in Järfälla.

About the role

We are looking for an experienced and structured Product Manager to play a key role in ensuring that Silex meets its On-Time Delivery goals while driving product- and production-related activities forward.

In this role, you will combine project and program management, production planning, and customer management. You will be the primary interface between production, customers, and external partners, taking responsibility for planning and coordinating products from order through delivery.

You will also drive and lead projects and improvement initiatives related to products, production processes, planning, and systems. As Product Owner, you will ensure that product-related requirements, priorities, data, and systems are aligned and continuously improved.

The role is highly cross-functional and requires someone with a strong technical interest, excellent communication skills, and the ability to create structure and drive activities forward in a complex production environment.

Key Responsibilities

• Act as the primary point of contact for customers and external partners regarding production-related matters, including delivery schedules, plans, status updates, and technical inquiries.

• Drive and lead projects and programs related to product, production, planning, and continuous improvement.

• Take product ownership, ensuring that product requirements, priorities, processes, data, and systems are clearly defined and maintained.

• Plan and manage products from order to delivery, ensuring efficient production flow and on-time delivery.

• Follow up on status and movement throughout the production cycle and proactively identify and resolve deviations.

• Monitor and control product starts, ensuring timely execution and delivery.

• Manage Work-In-Progress (WIP) updates and track production flow through the fab.

• Be responsible for Product Quality and ensure compliance with our Customer Product Integration (CPI) process.

• Coordinate activities across production, engineering, quality, planning, and other internal functions.

• Drive product improvements and development initiatives together with internal teams, customers, and partners.

• Support the development and implementation of new WIP management and planning tools.

• Ensure clear communication, documentation, follow-up, and structure throughout projects and product activities.

We are looking for someone who

We believe you have several years of experience driving and leading projects, preferably in a technical, manufacturing, or process-industry environment. You are comfortable taking ownership, coordinating multiple stakeholders, and driving activities from idea to implementation.

You have:

• An engineering Master’s degree or equivalent within materials, nanotechnology, physics, chemical engineering, or a similar field.

• Experience in project management and the ability to independently drive projects and programs to completion.

• Experience and knowledge of MEMS, semiconductor technology, or a closely related field is considered highly beneficial.

• Experience in production planning, product management, or technical project management.

• Experience of working with and managing customers and/or external partners.

• A strong technical interest and the ability to understand and communicate complex technical topics.

• Excellent communication skills and the ability to build strong relationships across different functions and organizational levels.

• A highly structured and organized way of working, with strong attention to detail and the ability to prioritize in a complex environment.

• A collaborative and solution-oriented mindset, combined with the confidence to take initiative and make decisions.

• Fluency in English; Swedish is highly meritorious.

Above all, we are looking for someone who enjoys working at the intersection of technology, production, customers, and business, and who is motivated by taking ownership and making things happen.

More about the recruitment process

We apply an ongoing recruitment process and therefore encourage you to submit your application as soon as possible. To apply, please attach your CV and answer a few questions.

In the process, we work with a competence-based approach, which will include a personality and logic test, as well as reference checks. We will also conduct both a background check and a drug test before employment. As an employer, we also carry out random alcohol and drug tests on all employees.

Silex Microsystems is the world’s leading pure-play MEMS foundry. We customize MEMS solutions for prominent global clients in industries such as pharmaceuticals, consumer electronics, and the automotive industry. We were founded in 2000 in a KTH lab by, among others, current CEO Edvard Kälvesten. Our headquarters and factory are located in Järfälla.

About the role

We are looking for an experienced and structured Product Manager to play a key role in ensuring that Silex meets its On-Time Delivery goals while driving product- and production-related activities forward.

In this role, you will combine project and program management, production planning, and customer management. You will be the primary interface between production, customers, and external partners, taking responsibility for planning and coordinating products from order through delivery.

You will also drive and lead projects and improvement initiatives related to products, production processes, planning, and systems. As Product Owner, you will ensure that product-related requirements, priorities, data, and systems are aligned and continuously improved.

The role is highly cross-functional and requires someone with a strong technical interest, excellent communication skills, and the ability to create structure and drive activities forward in a complex production environment.

Key Responsibilities

• Act as the primary point of contact for customers and external partners regarding production-related matters, including delivery schedules, plans, status updates, and technical inquiries.

• Drive and lead projects and programs related to product, production, planning, and continuous improvement.

• Take product ownership, ensuring that product requirements, priorities, processes, data, and systems are clearly defined and maintained.

• Plan and manage products from order to delivery, ensuring efficient production flow and on-time delivery.

• Follow up on status and movement throughout the production cycle and proactively identify and resolve deviations.

• Monitor and control product starts, ensuring timely execution and delivery.

• Manage Work-In-Progress (WIP) updates and track production flow through the fab.

• Be responsible for Product Quality and ensure compliance with our Customer Product Integration (CPI) process.

• Coordinate activities across production, engineering, quality, planning, and other internal functions.

• Drive product improvements and development initiatives together with internal teams, customers, and partners.

• Support the development and implementation of new WIP management and planning tools.

• Ensure clear communication, documentation, follow-up, and structure throughout projects and product activities.

We are looking for someone who

We believe you have several years of experience driving and leading projects, preferably in a technical, manufacturing, or process-industry environment. You are comfortable taking ownership, coordinating multiple stakeholders, and driving activities from idea to implementation.

You have:

• An engineering Master’s degree or equivalent within materials, nanotechnology, physics, chemical engineering, or a similar field.

• Experience in project management and the ability to independently drive projects and programs to completion.

• Experience and knowledge of MEMS, semiconductor technology, or a closely related field is considered highly beneficial.

• Experience in production planning, product management, or technical project management.

• Experience of working with and managing customers and/or external partners.

• A strong technical interest and the ability to understand and communicate complex technical topics.

• Excellent communication skills and the ability to build strong relationships across different functions and organizational levels.

• A highly structured and organized way of working, with strong attention to detail and the ability to prioritize in a complex environment.

• A collaborative and solution-oriented mindset, combined with the confidence to take initiative and make decisions.

• Fluency in English; Swedish is highly meritorious.

Above all, we are looking for someone who enjoys working at the intersection of technology, production, customers, and business, and who is motivated by taking ownership and making things happen.

More about the recruitment process

We apply an ongoing recruitment process and therefore encourage you to submit your application as soon as possible. To apply, please attach your CV and answer a few questions.

In the process, we work with a competence-based approach, which will include a personality and logic test, as well as reference checks. We will also conduct both a background check and a drug test before employment. As an employer, we also carry out random alcohol and drug tests on all employees.

Silex Microsystems is the world’s leading pure-play MEMS foundry. We customize MEMS solutions for prominent global clients in industries such as pharmaceuticals, consumer electronics, and the automotive industry. We were founded in 2000 in a KTH lab by, among others, current CEO Edvard Kälvesten. Our headquarters and factory are located in Järfälla.

About the role

We are looking for an experienced and structured Product Manager to play a key role in ensuring that Silex meets its On-Time Delivery goals while driving product- and production-related activities forward.

In this role, you will combine project and program management, production planning, and customer management. You will be the primary interface between production, customers, and external partners, taking responsibility for planning and coordinating products from order through delivery.

You will also drive and lead projects and improvement initiatives related to products, production processes, planning, and systems. As Product Owner, you will ensure that product-related requirements, priorities, data, and systems are aligned and continuously improved.

The role is highly cross-functional and requires someone with a strong technical interest, excellent communication skills, and the ability to create structure and drive activities forward in a complex production environment.

Key Responsibilities

• Act as the primary point of contact for customers and external partners regarding production-related matters, including delivery schedules, plans, status updates, and technical inquiries.

• Drive and lead projects and programs related to product, production, planning, and continuous improvement.

• Take product ownership, ensuring that product requirements, priorities, processes, data, and systems are clearly defined and maintained.

• Plan and manage products from order to delivery, ensuring efficient production flow and on-time delivery.

• Follow up on status and movement throughout the production cycle and proactively identify and resolve deviations.

• Monitor and control product starts, ensuring timely execution and delivery.

• Manage Work-In-Progress (WIP) updates and track production flow through the fab.

• Be responsible for Product Quality and ensure compliance with our Customer Product Integration (CPI) process.

• Coordinate activities across production, engineering, quality, planning, and other internal functions.

• Drive product improvements and development initiatives together with internal teams, customers, and partners.

• Support the development and implementation of new WIP management and planning tools.

• Ensure clear communication, documentation, follow-up, and structure throughout projects and product activities.

We are looking for someone who

We believe you have several years of experience driving and leading projects, preferably in a technical, manufacturing, or process-industry environment. You are comfortable taking ownership, coordinating multiple stakeholders, and driving activities from idea to implementation.

You have:

• An engineering Master’s degree or equivalent within materials, nanotechnology, physics, chemical engineering, or a similar field.

• Experience in project management and the ability to independently drive projects and programs to completion.

• Experience and knowledge of MEMS, semiconductor technology, or a closely related field is considered highly beneficial.

• Experience in production planning, product management, or technical project management.

• Experience of working with and managing customers and/or external partners.

• A strong technical interest and the ability to understand and communicate complex technical topics.

• Excellent communication skills and the ability to build strong relationships across different functions and organizational levels.

• A highly structured and organized way of working, with strong attention to detail and the ability to prioritize in a complex environment.

• A collaborative and solution-oriented mindset, combined with the confidence to take initiative and make decisions.

• Fluency in English; Swedish is highly meritorious.

Above all, we are looking for someone who enjoys working at the intersection of technology, production, customers, and business, and who is motivated by taking ownership and making things happen.

More about the recruitment process

We apply an ongoing recruitment process and therefore encourage you to submit your application as soon as possible. To apply, please attach your CV and answer a few questions.

In the process, we work with a competence-based approach, which will include a personality and logic test, as well as reference checks. We will also conduct both a background check and a drug test before employment. As an employer, we also carry out random alcohol and drug tests on all employees.

Project Description 

Background  Laser Ultrasonics (LUS) is a powerful non-contact technique for in-situ characterization of metallic materials at elevated temperatures. In hot strip mill environments, LUS enables real-time monitoring of grain size evolution through analysis of ultrasonic wave attenuation and velocity, providing critical insight into recrystallization, grain growth, and phase transformations. This information is highly valuable for advanced process control strategies aimed at improving product quality, consistency, and resource efficiency. 

The NAICE project (Non-destructive AI enhanced online grain size evaluation) addresses a key challenge in fossil free steel production: today’s steel mills lack real time information about the microstructure that governs material quality, leading to unnecessary energy use, scrap, and slow process adjustments. By upgrading the world’s only industrial laser ultrasonic grain size gauge and collecting a unique large dataset, the project will train models to enable AI enhanced measurement of the grain size and microstructure in real-time. This enables smarter process control, higher yield, and reduced emissions. 

This thesis is the first of two planned master thesis works. The first will evaluate the best options using historical data sets, whereas the second will focus on using the full dataset and optimizing the model for industrial implementation. 

If you want to know more about the LUS technology and the installation of the world’s only grain size gauge there is a recorded webinar here: https://www.youtube.com/watch?v=ZOpXUAPhEv0&t=665s  

Thesis Scope  The thesis will focus on the application of machine learning and AI techniques to Laser Ultrasonic (LUS) data acquired in a hot strip mill environment. The available dataset consists of: 

• Raw LUS A-scan signals with multiple backwall echoes (20 µs duration, 1 ns resolution) 
• Grain size values calculated using an established conventional algorithm (serving as reference values) 
• The primary objective is to develop and evaluate AI-based approaches for:  - Grain size prediction directly from raw laser ultrasonic signals  - Microstructure state classification (e.g., recrystallized fraction, pancaking/aspect ratio) 

A key part of the work will be to survey suitable AI architectures, such as time-series models, deep learning approaches, and alternative signal-processing-informed models, and assess their applicability to the LUS data. 

Tasks 

• Survey and critically assess relevant AI/ML architectures for ultrasonic time-series data 
• Pre-process and structure high-resolution LUS A-scan datasets for model training 
• Develop and implement selected AI models 
• Perform benchmark comparisons between different approaches with respect to:  
• Prediction accuracy 
• Robustness to signal variability 
• Computational efficiency and suitability for real-time deployment 
• Analyze model interpretability in relation to known physical phenomena 

Expected Outcomes 

The thesis should deliver: 

• A structured comparison of candidate AI approaches for LUS-based grain size prediction 
• Prototype models demonstrating feasibility on the historical dataset 
• Recommendations for model selection in future industrial implementation 

Desired Qualifications  We are looking for a motivated student with: 

• A background in computer engineering, engineering physics, materials science, applied mathematics, or similar 
• Strong interest in machine learning and signal processing 
• Experience with Python/Matlab and ML frameworks 

• Basic understanding of wave physics, ultrasonics, or materials science (meriting but not required) 
• Analytical mindset and ability to work with complex datasets 

Additional Information 

• The work will be carried out in close collaboration with experts in Laser Ultrasonics and industrial process monitoring, providing a unique opportunity to contribute to cutting-edge development of AI-driven process control in steel manufacturing. 

Project time  The project is intended for a master thesis (30hp), starting in spring 2027 or can be mutually decided through negotiations. 

Further information  This project is intended to be performed at Swerim in Stockholm. Swerim rewards the student with 50 000 SEK for an approved master thesis (30hp). 

Contacts  For further information please contact:  

• Mikael Malmström, mikael.malmstrom@swerim.se 
• Hampus Wikmark Kreuger, hampus.wikmark@swerim.se 

Application  Apply by using the application function below. The application can be written in Swedish (or English). You will receive a confirmation that Swerim has received your application. Please note that we fill the position as soon as we find a suitable applicant, which means there is no explicit deadline. 

Project Description 

Background  Laser Ultrasonics (LUS) is a powerful non-contact technique for in-situ characterization of metallic materials at elevated temperatures. In hot strip mill environments, LUS enables real-time monitoring of grain size evolution through analysis of ultrasonic wave attenuation and velocity, providing critical insight into recrystallization, grain growth, and phase transformations. This information is highly valuable for advanced process control strategies aimed at improving product quality, consistency, and resource efficiency. 

The NAICE project (Non-destructive AI enhanced online grain size evaluation) addresses a key challenge in fossil free steel production: today’s steel mills lack real time information about the microstructure that governs material quality, leading to unnecessary energy use, scrap, and slow process adjustments. By upgrading the world’s only industrial laser ultrasonic grain size gauge and collecting a unique large dataset, the project will train models to enable AI enhanced measurement of the grain size and microstructure in real-time. This enables smarter process control, higher yield, and reduced emissions. 

This thesis is the first of two planned master thesis works. The first will evaluate the best options using historical data sets, whereas the second will focus on using the full dataset and optimizing the model for industrial implementation. 

If you want to know more about the LUS technology and the installation of the world’s only grain size gauge there is a recorded webinar here: https://www.youtube.com/watch?v=ZOpXUAPhEv0&t=665s  

Thesis Scope  The thesis will focus on the application of machine learning and AI techniques to Laser Ultrasonic (LUS) data acquired in a hot strip mill environment. The available dataset consists of: 

• Raw LUS A-scan signals with multiple backwall echoes (20 µs duration, 1 ns resolution) 
• Grain size values calculated using an established conventional algorithm (serving as reference values) 
• The primary objective is to develop and evaluate AI-based approaches for:  - Grain size prediction directly from raw laser ultrasonic signals  - Microstructure state classification (e.g., recrystallized fraction, pancaking/aspect ratio) 

A key part of the work will be to survey suitable AI architectures, such as time-series models, deep learning approaches, and alternative signal-processing-informed models, and assess their applicability to the LUS data. 

Tasks 

• Survey and critically assess relevant AI/ML architectures for ultrasonic time-series data 
• Pre-process and structure high-resolution LUS A-scan datasets for model training 
• Develop and implement selected AI models 
• Perform benchmark comparisons between different approaches with respect to:  
• Prediction accuracy 
• Robustness to signal variability 
• Computational efficiency and suitability for real-time deployment 
• Analyze model interpretability in relation to known physical phenomena 

Expected Outcomes 

The thesis should deliver: 

• A structured comparison of candidate AI approaches for LUS-based grain size prediction 
• Prototype models demonstrating feasibility on the historical dataset 
• Recommendations for model selection in future industrial implementation 

Desired Qualifications  We are looking for a motivated student with: 

• A background in computer engineering, engineering physics, materials science, applied mathematics, or similar 
• Strong interest in machine learning and signal processing 
• Experience with Python/Matlab and ML frameworks 

• Basic understanding of wave physics, ultrasonics, or materials science (meriting but not required) 
• Analytical mindset and ability to work with complex datasets 

Additional Information 

• The work will be carried out in close collaboration with experts in Laser Ultrasonics and industrial process monitoring, providing a unique opportunity to contribute to cutting-edge development of AI-driven process control in steel manufacturing. 

Project time  The project is intended for a master thesis (30hp), starting in spring 2027 or can be mutually decided through negotiations. 

Further information  This project is intended to be performed at Swerim in Stockholm. Swerim rewards the student with 50 000 SEK for an approved master thesis (30hp). 

Contacts  For further information please contact:  

• Mikael Malmström, mikael.malmstrom@swerim.se 
• Hampus Wikmark Kreuger, hampus.wikmark@swerim.se 

Application  Apply by using the application function below. The application can be written in Swedish (or English). You will receive a confirmation that Swerim has received your application. Please note that we fill the position as soon as we find a suitable applicant, which means there is no explicit deadline. 

Project Description 

Background  Laser Ultrasonics (LUS) is a powerful non-contact technique for in-situ characterization of metallic materials at elevated temperatures. In hot strip mill environments, LUS enables real-time monitoring of grain size evolution through analysis of ultrasonic wave attenuation and velocity, providing critical insight into recrystallization, grain growth, and phase transformations. This information is highly valuable for advanced process control strategies aimed at improving product quality, consistency, and resource efficiency. 

The NAICE project (Non-destructive AI enhanced online grain size evaluation) addresses a key challenge in fossil free steel production: today’s steel mills lack real time information about the microstructure that governs material quality, leading to unnecessary energy use, scrap, and slow process adjustments. By upgrading the world’s only industrial laser ultrasonic grain size gauge and collecting a unique large dataset, the project will train models to enable AI enhanced measurement of the grain size and microstructure in real-time. This enables smarter process control, higher yield, and reduced emissions. 

This thesis is the first of two planned master thesis works. The first will evaluate the best options using historical data sets, whereas the second will focus on using the full dataset and optimizing the model for industrial implementation. 

If you want to know more about the LUS technology and the installation of the world’s only grain size gauge there is a recorded webinar here: https://www.youtube.com/watch?v=ZOpXUAPhEv0&t=665s  

Thesis Scope  The thesis will focus on the application of machine learning and AI techniques to Laser Ultrasonic (LUS) data acquired in a hot strip mill environment. The available dataset consists of: 

• Raw LUS A-scan signals with multiple backwall echoes (20 µs duration, 1 ns resolution) 
• Grain size values calculated using an established conventional algorithm (serving as reference values) 
• The primary objective is to develop and evaluate AI-based approaches for:  - Grain size prediction directly from raw laser ultrasonic signals  - Microstructure state classification (e.g., recrystallized fraction, pancaking/aspect ratio) 

A key part of the work will be to survey suitable AI architectures, such as time-series models, deep learning approaches, and alternative signal-processing-informed models, and assess their applicability to the LUS data. 

Tasks 

• Survey and critically assess relevant AI/ML architectures for ultrasonic time-series data 
• Pre-process and structure high-resolution LUS A-scan datasets for model training 
• Develop and implement selected AI models 
• Perform benchmark comparisons between different approaches with respect to:  
• Prediction accuracy 
• Robustness to signal variability 
• Computational efficiency and suitability for real-time deployment 
• Analyze model interpretability in relation to known physical phenomena 

Expected Outcomes 

The thesis should deliver: 

• A structured comparison of candidate AI approaches for LUS-based grain size prediction 
• Prototype models demonstrating feasibility on the historical dataset 
• Recommendations for model selection in future industrial implementation 

Desired Qualifications  We are looking for a motivated student with: 

• A background in computer engineering, engineering physics, materials science, applied mathematics, or similar 
• Strong interest in machine learning and signal processing 
• Experience with Python/Matlab and ML frameworks 

• Basic understanding of wave physics, ultrasonics, or materials science (meriting but not required) 
• Analytical mindset and ability to work with complex datasets 

Additional Information 

• The work will be carried out in close collaboration with experts in Laser Ultrasonics and industrial process monitoring, providing a unique opportunity to contribute to cutting-edge development of AI-driven process control in steel manufacturing. 

Project time  The project is intended for a master thesis (30hp), starting in spring 2027 or can be mutually decided through negotiations. 

Further information  This project is intended to be performed at Swerim in Stockholm. Swerim rewards the student with 50 000 SEK for an approved master thesis (30hp). 

Contacts  For further information please contact:  

• Mikael Malmström, mikael.malmstrom@swerim.se 
• Hampus Wikmark Kreuger, hampus.wikmark@swerim.se 

Application  Apply by using the application function below. The application can be written in Swedish (or English). You will receive a confirmation that Swerim has received your application. Please note that we fill the position as soon as we find a suitable applicant, which means there is no explicit deadline. 

Project Description 

Background  Laser Ultrasonics (LUS) is a powerful non-contact technique for in-situ characterization of metallic materials at elevated temperatures. In hot strip mill environments, LUS enables real-time monitoring of grain size evolution through analysis of ultrasonic wave attenuation and velocity, providing critical insight into recrystallization, grain growth, and phase transformations. This information is highly valuable for advanced process control strategies aimed at improving product quality, consistency, and resource efficiency. 

The NAICE project (Non-destructive AI enhanced online grain size evaluation) addresses a key challenge in fossil free steel production: today’s steel mills lack real time information about the microstructure that governs material quality, leading to unnecessary energy use, scrap, and slow process adjustments. By upgrading the world’s only industrial laser ultrasonic grain size gauge and collecting a unique large dataset, the project will train models to enable AI enhanced measurement of the grain size and microstructure in real-time. This enables smarter process control, higher yield, and reduced emissions. 

This thesis is the first of two planned master thesis works. The first will evaluate the best options using historical data sets, whereas the second will focus on using the full dataset and optimizing the model for industrial implementation. 

If you want to know more about the LUS technology and the installation of the world’s only grain size gauge there is a recorded webinar here: https://www.youtube.com/watch?v=ZOpXUAPhEv0&t=665s  

Thesis Scope  The thesis will focus on the application of machine learning and AI techniques to Laser Ultrasonic (LUS) data acquired in a hot strip mill environment. The available dataset consists of: 

• Raw LUS A-scan signals with multiple backwall echoes (20 µs duration, 1 ns resolution) 
• Grain size values calculated using an established conventional algorithm (serving as reference values) 
• The primary objective is to develop and evaluate AI-based approaches for:  - Grain size prediction directly from raw laser ultrasonic signals  - Microstructure state classification (e.g., recrystallized fraction, pancaking/aspect ratio) 

A key part of the work will be to survey suitable AI architectures, such as time-series models, deep learning approaches, and alternative signal-processing-informed models, and assess their applicability to the LUS data. 

Tasks 

• Survey and critically assess relevant AI/ML architectures for ultrasonic time-series data 
• Pre-process and structure high-resolution LUS A-scan datasets for model training 
• Develop and implement selected AI models 
• Perform benchmark comparisons between different approaches with respect to:  
• Prediction accuracy 
• Robustness to signal variability 
• Computational efficiency and suitability for real-time deployment 
• Analyze model interpretability in relation to known physical phenomena 

Expected Outcomes 

The thesis should deliver: 

• A structured comparison of candidate AI approaches for LUS-based grain size prediction 
• Prototype models demonstrating feasibility on the historical dataset 
• Recommendations for model selection in future industrial implementation 

Desired Qualifications  We are looking for a motivated student with: 

• A background in computer engineering, engineering physics, materials science, applied mathematics, or similar 
• Strong interest in machine learning and signal processing 
• Experience with Python/Matlab and ML frameworks 

• Basic understanding of wave physics, ultrasonics, or materials science (meriting but not required) 
• Analytical mindset and ability to work with complex datasets 

Additional Information 

• The work will be carried out in close collaboration with experts in Laser Ultrasonics and industrial process monitoring, providing a unique opportunity to contribute to cutting-edge development of AI-driven process control in steel manufacturing. 

Project time  The project is intended for a master thesis (30hp), starting in spring 2027 or can be mutually decided through negotiations. 

Further information  This project is intended to be performed at Swerim in Stockholm. Swerim rewards the student with 50 000 SEK for an approved master thesis (30hp). 

Contacts  For further information please contact:  

• Mikael Malmström, mikael.malmstrom@swerim.se 
• Hampus Wikmark Kreuger, hampus.wikmark@swerim.se 

Application  Apply by using the application function below. The application can be written in Swedish (or English). You will receive a confirmation that Swerim has received your application. Please note that we fill the position as soon as we find a suitable applicant, which means there is no explicit deadline. 

Project Description 

Background  Laser Ultrasonics (LUS) is a powerful non-contact technique for in-situ characterization of metallic materials at elevated temperatures. In hot strip mill environments, LUS enables real-time monitoring of grain size evolution through analysis of ultrasonic wave attenuation and velocity, providing critical insight into recrystallization, grain growth, and phase transformations. This information is highly valuable for advanced process control strategies aimed at improving product quality, consistency, and resource efficiency. 

The NAICE project (Non-destructive AI enhanced online grain size evaluation) addresses a key challenge in fossil free steel production: today’s steel mills lack real time information about the microstructure that governs material quality, leading to unnecessary energy use, scrap, and slow process adjustments. By upgrading the world’s only industrial laser ultrasonic grain size gauge and collecting a unique large dataset, the project will train models to enable AI enhanced measurement of the grain size and microstructure in real-time. This enables smarter process control, higher yield, and reduced emissions. 

This thesis is the first of two planned master thesis works. The first will evaluate the best options using historical data sets, whereas the second will focus on using the full dataset and optimizing the model for industrial implementation. 

If you want to know more about the LUS technology and the installation of the world’s only grain size gauge there is a recorded webinar here: https://www.youtube.com/watch?v=ZOpXUAPhEv0&t=665s  

Thesis Scope  The thesis will focus on the application of machine learning and AI techniques to Laser Ultrasonic (LUS) data acquired in a hot strip mill environment. The available dataset consists of: 

• Raw LUS A-scan signals with multiple backwall echoes (20 µs duration, 1 ns resolution) 
• Grain size values calculated using an established conventional algorithm (serving as reference values) 
• The primary objective is to develop and evaluate AI-based approaches for:  - Grain size prediction directly from raw laser ultrasonic signals  - Microstructure state classification (e.g., recrystallized fraction, pancaking/aspect ratio) 

A key part of the work will be to survey suitable AI architectures, such as time-series models, deep learning approaches, and alternative signal-processing-informed models, and assess their applicability to the LUS data. 

Tasks 

• Survey and critically assess relevant AI/ML architectures for ultrasonic time-series data 
• Pre-process and structure high-resolution LUS A-scan datasets for model training 
• Develop and implement selected AI models 
• Perform benchmark comparisons between different approaches with respect to:  
• Prediction accuracy 
• Robustness to signal variability 
• Computational efficiency and suitability for real-time deployment 
• Analyze model interpretability in relation to known physical phenomena 

Expected Outcomes 

The thesis should deliver: 

• A structured comparison of candidate AI approaches for LUS-based grain size prediction 
• Prototype models demonstrating feasibility on the historical dataset 
• Recommendations for model selection in future industrial implementation 

Desired Qualifications  We are looking for a motivated student with: 

• A background in computer engineering, engineering physics, materials science, applied mathematics, or similar 
• Strong interest in machine learning and signal processing 
• Experience with Python/Matlab and ML frameworks 

• Basic understanding of wave physics, ultrasonics, or materials science (meriting but not required) 
• Analytical mindset and ability to work with complex datasets 

Additional Information 

• The work will be carried out in close collaboration with experts in Laser Ultrasonics and industrial process monitoring, providing a unique opportunity to contribute to cutting-edge development of AI-driven process control in steel manufacturing. 

Project time  The project is intended for a master thesis (30hp), starting in spring 2027 or can be mutually decided through negotiations. 

Further information  This project is intended to be performed at Swerim in Stockholm. Swerim rewards the student with 50 000 SEK for an approved master thesis (30hp). 

Contacts  For further information please contact:  

• Mikael Malmström, mikael.malmstrom@swerim.se 
• Hampus Wikmark Kreuger, hampus.wikmark@swerim.se 

Application  Apply by using the application function below. The application can be written in Swedish (or English). You will receive a confirmation that Swerim has received your application. Please note that we fill the position as soon as we find a suitable applicant, which means there is no explicit deadline.