PhD in High-Frequency Transformer Design and Partial Discharge Modeling for Data Centers

Introduction
Worldwide, we consume vast amounts of electricity, with data centers and compute currently accounting for more than 2% of global consumption. This share is growing exponentially year-over-year. Within a decade, compute could consume as much as 10%. Are you on board with us to embrace the challenge of designing next-generation power transformers for AI data centers? Are you our next PhD candidate to work on enablers of future compute — the high-frequency transformers?

The EPE group at TU/e seeks a motivated PhD researcher to advance the state-of-the-art in power electronics transformers (PETs) for data center applications. This position offers a unique opportunity to work at the intersection of high-voltage engineering, high frequency transformer, and AI-driven design optimization, with direct industrial collaboration and real-world impact on the sustainability of AI infrastructure.

Job Description
You will enroll as a PhD candidate in the group of Electromechanics and Power Electronics (EPE) at TU/e, in close collaboration with Delta Energy System GmbH, a leading industrial partner in power electronics for data centers.

Your research will be concerned with developing high-frequency, medium-voltage transformer architectures supported by partial discharge (PD) modeling methodologies that enable >99.5% efficiency, >10kV operation, and tens fold copper reduction compared to conventional designs. Your research will directly address the power density and efficiency challenges faced by modern data centers powering AI and machine learning workloads.

You will aim at advancing the state-of-the-art in solid state transformers for data center applications. This includes designing high-frequency transformer architectures with novel insulation concepts, prone to high voltage stresses and endure the accelerated ageing through developing predictive models for partial discharge behavior, and validating designs through simulation and experimental testing. The goal is to create transformers that achieve unprecedented efficiency, power density, and reliability for AI infrastructure.

You will have access to state-of-the-art simulation tools (ANSYS Maxwell, COMSOL) and high-voltage testing facilities. Research results will contribute to making AI data centers more sustainable by dramatically improving the efficiency and power density of their power delivery systems. With data center energy consumption growing exponentially due to AI/ML workloads, your research has direct impact on reducing the carbon footprint of digital infrastructure.

Job Requirements

• A master's degree (or equivalent university degree) in Electrical Engineering, Electromechanics, Power Electronics, High-Voltage Engineering, or a related field
• A research-oriented attitude with strong analytical and problem-solving skills
• Ability, with demonstrated experience, to work in an interdisciplinary team and interest in collaborating with industrial partners and other stakeholders
• Motivated to develop your teaching skills and coach students
• Fluent in spoken and written English (C1 level or equivalent)

Based on our candidate assessment, the following skills are valuable:

Required:

• Demonstrated kowledge in the physics of transformers, magnetism. Familiarity with numerical tools (ANSYS Maxwell, COMSOL, or similar). Knowledge of mathematics behind Finite Element Method, and manipulation of custom numerical toolboxes is a strong asset
• Affinity with high-voltage engineering, fundamentals and insulation system understanding.
• Understanding of power electronics and transformer design principles

Advantageous:

• Experience with partial discharge phenomena and measurement equipment, and other PD peculiarities.
• Knowledge of basic ML methods and accompanying tools such as (Python, TensorFlow, PyTorch) for predictive modeling.
• Familiarity with multiphysics strong and/or weak coupling.
• Hands-on experimental validation experience in high-voltage laboratories.

Conditions of Employment
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station.

In addition, we offer you:

• Full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. € 3,204 - max. € 4,051 gross base salary per month (full-time)).
• In addition to your base salary, you will receive an 8% holiday allowance and an 8.3% year-end bonus, both calculated based on your annual gross base salary.
• Generous leave options: a standard 29 days (based on a 38 hour working week) per year that you can increase to 41 days by working two hours more per week (flexible working hours). This is prorated if you work part-time.
• As a TU/e employee, you participate in the ABP pension scheme, providing retirement pension and pension benefits for surviving dependents and occupational disability. TU/e pays 70% of the pension premium, while employees contribute the remaining 30%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, and on-campus children's day care.
• Unlimited access to the modern on‑campus TU/e Student Sports Center at an exceptionally affordable rate.
• We support your wellbeing with free 24/7 access to OpenUp, providing you and your family with mental health support, expert guidance, and online training.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the Expat Scheme) for international candidates.

On our website you can discover even more information about our conditions of employment. Build on your career at TU/e!

About us
We are a leading international university where scientific curiosity meets a hands-on mindset. We work in an open and collaborative way with high-tech industries to tackle complex societal challenges. Our responsible and respectful approach ensures impact — today and in the future. TU/e is home to over 13,000 students and more than 7,000 staff, forming a diverse and vibrant academic community.

Our university is located in Brainport Eindhoven — a world‑leading tech region with more than 7,000 high‑tech companies and strong R&D activity. Known for breakthroughs in AI, photonics, semiconductors and advanced manufacturing, Brainport is a place where technology serves people and society. Learn more about the Brainport region here.

Information
Do you recognize yourself in this profile, do not hesitate to apply directly through our web platform. For additional information please contact:

• dr. Levy Costa, Project leader, l.costa@tue.nl.
• dr. Mitrofan Curti, Direct supervisor, m.curti@tue.nl.

Visit our website for more information about the application process. You can also contact HRServices.ee@tue.nl.

Curious to hear more about what it’s like as a PhD candidate at TU/e? Please view the video.

Are you inspired and would like to know more about working at TU/e? Please visit our career page.

Application
We invite you to submit a complete application by using the apply button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• A factual, position-focused curriculum vitae, including a list of your publications and the contact information of three references. Kindly note that we may reach out to references at any stage of the recruitment process. We recommend notifying your references upon submitting your application.

Ensure that you submit all the requested application documents. We give priority to complete applications.

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.

Please note

• You can apply online. We will not process applications sent by email and/or post.
• A pre-employment screening (e.g. knowledge security check) can be part of the selection procedure. For more information on the knowledge security check, please consult the National Knowledge Security Guidelines.
• Please do not contact us for unsolicited services.

PhD in High-Frequency Transformer Design and Partial Discharge Modeling for Data Centers

You will enroll as a PhD candidate in the group of Electromechanics and Power Electronics (EPE) at TU/e, in close collaboration with Delta Energy System GmbH, a leading industrial partner in power electronics for data centers.

Your research will be concerned with developing high-frequency, medium-voltage transformer architectures supported by partial discharge (PD) modeling methodologies that enable >99.5% efficiency, >10kV operation, and tens fold copper reduction compared to conventional designs. Your research will directly address the power density and efficiency challenges faced by modern data centers powering AI and machine learning workloads.

You will aim at advancing the state-of-the-art in solid state transformers for data center applications. This includes designing high-frequency transformer architectures with novel insulation concepts, prone to high voltage stresses and endure the accelerated ageing through developing predictive models for partial discharge behavior, and validating designs through simulation and experimental testing. The goal is to create transformers that achieve unprecedented efficiency, power density, and reliability for AI infrastructure.

You will have access to state-of-the-art simulation tools (ANSYS Maxwell, COMSOL) and high-voltage testing facilities. Research results will contribute to making AI data centers more sustainable by dramatically improving the efficiency and power density of their power delivery systems. With data center energy consumption growing exponentially due to AI/ML workloads, your research has direct impact on reducing the carbon footprint of digital infrastructure.

PhD in Hardware Architectures for Physics-Informed AI

Introduction
Are you passionate about combining artificial intelligence, physics-based modelling, and digital hardware design? Do you want to develop the next generation of FPGA/ASIC-based computing platforms that make physics-informed AI fast, efficient, and deployable in real-world mission-critical sensing systems?

Job Description
Physics-informed AI is emerging as a powerful alternative to purely data-driven machine learning. By incorporating physical models, domain knowledge, and optimization algorithms directly into learning systems, physics-informed AI can achieve higher accuracy, better generalization, greater interpretability, and significantly reduced training-data requirements. Despite these advantages, many physics-informed AI methods remain computationally demanding and are often developed without considering efficient hardware implementation.

In this PhD project, you will investigate novel hardware architectures for physics-informed AI models, with a focus on FPGA/ASIC-based acceleration and edge deployment. The research will explore how hybrid model-based and learning-based algorithms can be mapped efficiently onto reconfigurable hardware platforms, enabling real-time operation in scientific, industrial, and sensing applications. You will be part of a multidisciplinary collaboration with the EE department and you will contribute to the NWO OTP project "Detection of Hidden Cash using Physics-Based AI" through algorithm development, hardware architecture design, and hardware-software co-design, and you will have opportunities to contribute to both fundamental research and practical demonstrators.

One of the project goals is to build a real-time demonstrator of counterfeit cash detection using Physics-Infused Deep Unfolding (PIDU) in collaboration with project stakeholders like Smiths Detection, Sioux Technologies, and the Dutch Customs. While PIDU will serve as an important research vehicle, the PhD will not be limited to this framework. The broader objective is to develop design methodologies and hardware architectures applicable to a wide range of physics-informed AI techniques, including deep unfolding, model-based learning, hybrid optimization-learning methods, and physics-informed neural networks.

Job Requirements

• A master’s degree (or an equivalent university degree in Electrical Engineering, Computer Engineering, Embedded Systems, Computer Science, or a related field).
• A research-oriented attitude.
• Ability to work in an interdisciplinary team and interested in collaborating with industrial partners.
• Motivated to develop your teaching skills and coach students.
• Fluent in spoken and written English (C1 level).
• Experience with FPGA/ASIC development and hardware description languages.
• Experience with machine learning, signal processing, and AI accelerators.
• Programming experience in Python and/or C/C++.

Conditions of Employment
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station.

In addition, we offer you:

• Full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. € 3,204 - max. € 4,051 gross base salary per month (full-time)).
• In addition to your base salary, you will receive an 8% holiday allowance and an 8.3% year-end bonus, both calculated based on your annual gross base salary.
• Generous leave options: a standard 29 days (based on a 38 hour working week) per year that you can increase to 41 days by working two hours more per week (flexible working hours). This is prorated if you work part-time.
• As a TU/e employee, you participate in the ABP pension scheme, providing retirement pension and pension benefits for surviving dependents and occupational disability. TU/e pays 70% of the pension premium, while employees contribute the remaining 30%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, and on-campus children's day care.
• Unlimited access to the modern on‑campus TU/e Student Sports Center at an exceptionally affordable rate.
• We support your wellbeing with free 24/7 access to OpenUp, providing you and your family with mental health support, expert guidance, and online training.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the Expat Scheme) for international candidates.

On our website you can discover even more information about our conditions of employment. Build on your career at TU/e!

About us
We are a leading international university where scientific curiosity meets a hands-on mindset. We work in an open and collaborative way with high-tech industries to tackle complex societal challenges. Our responsible and respectful approach ensures impact — today and in the future. TU/e is home to over 13,000 students and more than 7,000 staff, forming a diverse and vibrant academic community.

Our university is located in Brainport Eindhoven — a world‑leading tech region with more than 7,000 high‑tech companies and strong R&D activity. Known for breakthroughs in AI, photonics, semiconductors and advanced manufacturing, Brainport is a place where technology serves people and society. Learn more about the Brainport region here.

Information
Do you recognize yourself in this profile and would you like to know more? Please contact the prospective PhD supervisor Prof. Alexios Balatsoukas Stimming (a.k.balatsoukas.stimming@tue.nl).

Visit our website for more information about the application process. You can also contact HRServices.ee@tue.nl.

Curious to hear more about what it’s like as a PhD candidate at TU/e? Please view the video.

Are you inspired and would like to know more about working at TU/e? Please visit our career page.

Application
We invite you to submit a complete application by using the apply button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Curriculum vitae, including a list of your publications and the contact information of three references. Kindly note that we may reach out to references at any stage of the recruitment process. We recommend notifying your references upon submitting your application.

Ensure that you submit all the requested application documents. We give priority to complete applications.

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.

Please note

• You can apply online. We will not process applications sent by email and/or post.
• A pre-employment screening (e.g. knowledge security check) can be part of the selection procedure. For more information on the knowledge security check, please consult the National Knowledge Security Guidelines.
• Please do not contact us for unsolicited services.

PhD in Hardware Architectures for Physics-Informed AI

Physics-informed AI is emerging as a powerful alternative to purely data-driven machine learning. By incorporating physical models, domain knowledge, and optimization algorithms directly into learning systems, physics-informed AI can achieve higher accuracy, better generalization, greater interpretability, and significantly reduced training-data requirements. Despite these advantages, many physics-informed AI methods remain computationally demanding and are often developed without considering efficient hardware implementation.

In this PhD project, you will investigate novel hardware architectures for physics-informed AI models, with a focus on FPGA/ASIC-based acceleration and edge deployment. The research will explore how hybrid model-based and learning-based algorithms can be mapped efficiently onto reconfigurable hardware platforms, enabling real-time operation in scientific, industrial, and sensing applications. You will be part of a multidisciplinary collaboration with the EE department and you will contribute to the NWO OTP project "Detection of Hidden Cash using Physics-Based AI" through algorithm development, hardware architecture design, and hardware-software co-design, and you will have opportunities to contribute to both fundamental research and practical demonstrators.

One of the project goals is to build a real-time demonstrator of counterfeit cash detection using Physics-Infused Deep Unfolding (PIDU) in collaboration with project stakeholders like Smiths Detection, Sioux Technologies, and the Dutch Customs. While PIDU will serve as an important research vehicle, the PhD will not be limited to this framework. The broader objective is to develop design methodologies and hardware architectures applicable to a wide range of physics-informed AI techniques, including deep unfolding, model-based learning, hybrid optimization-learning methods, and physics-informed neural networks.

PhD Universal mechanobiological gateways to Cardiac Regeneration across regenerative and non-regenerative species

Introduction
After a heart attack, the human heart heals largely by scarring rather than true regeneration. This PhD project aims to uncover the mechanobiological rules that push cardiac tissue toward pathological fibrosis in most animal species and toward more regenerative repair in rare animal species. You will engineer and study cross-species cardiac microtissue models and quantify how defined mechanical and biochemical cues from the cellular microenvironment shape mechanobiological tissue response to injury. You will then ambitiously use these quantitative insights to build predictive, testable mechanobiological hypotheses and identify intervention points to steer microtissue repair responses toward a more regenerative outcome.

Job Description
The research will be conducted in the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e) under the supervision of Prof.dr. Carlijn Bouten and Dr. Vito Conte, within the Soft Tissue Engineering and Mechanobiology (STEM) group. Prof. Bouten’s research aims to understand and harness how cells interact with their mechanical and biochemical environments to engineer regenerative, self-guided living tissues, while Dr. Conte’s research applies principles from physics and engineering to decipher how tissues organize and remodel in health and disease using in vitro, in vivo, and in silico approaches. In this PhD project, you will develop engineered, cross-species cardiac microtissue models to uncover mechanobiological rules that bias repair toward fibrosis in most species versus more regenerative healing in rare species. Using controlled injury assays and quantitative readouts of tissue organization, mechanics, and signaling, you will build predictive, testable hypotheses and evaluate microenvironmental strategies to steer repair responses. As part of the STEM group, you will have access to the Laboratory for Cell and Tissue Engineering and state-of-the-art infrastructure at the international forefront of engineered living tissues. This project is experimental at its core and requires a candidate with a strong background in biomedical engineering or mechanobiology. It demands confidence with quantitative experimentation, mammalian cell culture and microscopy, and an interest in combining wet-lab work with advanced analysis and modelling of complex datasets. Experience with biomaterials, microtissue engineering, mechanobiological assays and/or image-based quantification is advantageous but not essential.

Job Requirements
We are looking for enthusiastic and talented candidates to join our growing and ambitious research team. You should have/be:

• A master’s degree (or an equivalent university degree) in biomedical engineering, (bio)physics or bioengineering;
• Demonstrable experience with mammalian cell culture and experimental assays, preferably including microscopy and quantitative data analysis;
• A research-oriented attitude;
• Ability to work in an interdisciplinary team and effectively communicate scientific ideas, foster collaborations, and a capability for independent thinking;
• Motivated to develop your teaching skills and coach students;
• Fluent in spoken and written English (C1 level).

Conditions of Employment
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station.

In addition, we offer you:

• Full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. € 3,204 - max. € 4,051 gross base salary per month (full-time)).
• In addition to your base salary, you will receive an 8% holiday allowance and an 8.3% year-end bonus, both calculated based on your annual gross base salary.
• Generous leave options: a standard 29 days (based on a 38 hour working week) per year that you can increase to 41 days by working two hours more per week (flexible working hours). This is prorated if you work part-time.
• As a TU/e employee, you participate in the ABP pension scheme, providing retirement pension and pension benefits for surviving dependents and occupational disability. TU/e pays 70% of the pension premium, while employees contribute the remaining 30%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, and on-campus children's day care.
• Unlimited access to the modern on‑campus TU/e Student Sports Center at an exceptionally affordable rate.
• We support your wellbeing with free 24/7 access to OpenUp, providing you and your family with mental health support, expert guidance, and online training.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the Expat Scheme) for international candidates.

On our website you can discover even more information about our conditions of employment. Build on your career at TU/e!

About us
We are a leading international university where scientific curiosity meets a hands-on mindset. We work in an open and collaborative way with high-tech industries to tackle complex societal challenges. Our responsible and respectful approach ensures impact — today and in the future. TU/e is home to over 13,000 students and more than 7,000 staff, forming a diverse and vibrant academic community.

Our university is located in Brainport Eindhoven — a world‑leading tech region with more than 7,000 high‑tech companies and strong R&D activity. Known for breakthroughs in AI, photonics, semiconductors and advanced manufacturing, Brainport is a place where technology serves people and society. Learn more about the Brainport region here.

The Department of Biomedical Engineering offers top-level education and research in one of the most relevant and exciting scientific disciplines of the 21st century: engineering health. In combining engineering and life sciences, through challenge-based learning and a multidisciplinary approach in collaboration with hospitals, industry and others, the department addresses the great challenges of the future, striving to improve healthcare and society as a whole.

Information
Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager Dr. Vito Conte, v.conte@tue.nl and Prof. dr. Carlijn Bouten, C.V.C.Bouten@tue.nl.

Visit our website for more information about the application process. You can also contact HR advisors, hradvicebme@tue.nl.

Curious to hear more about what it’s like as a PhD candidate at TU/e? Please view the video.

Are you inspired and would like to know more about working at TU/e? Please visit our career page.

Application
We invite you to submit a complete application by using the apply button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Curriculum vitae, including a list of your publications and the contact information of three references. Kindly note that we may reach out to references at any stage of the recruitment process. We recommend notifying your references upon submitting your application.
• Academic transcript.
• A digital copy of your MSc thesis (if applicable).

Ensure that you submit all the requested application documents. We give priority to complete applications.

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.

Please note

• You can apply online. We will not process applications sent by email and/or post.
• A pre-employment screening (e.g. knowledge security check) can be part of the selection procedure. For more information on the knowledge security check, please consult the National Knowledge Security Guidelines.
• Please do not contact us for unsolicited services.