Key Skills

Job Description

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). 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 ([email protected]). Visit our website for more information about the application process. You can also contact [email protected]. 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.

Core Responsibilities

The candidate will investigate and develop novel hardware architectures for physics-informed AI models, focusing on FPGA/ASIC-based acceleration and edge deployment. They will contribute to multidisciplinary research projects, including algorithm development and the creation of real-time demonstrators for industrial applications.

Requirements

Applicants must hold a master's degree and possess experience in FPGA/ASIC development, machine learning, and programming in Python or C/C++. A research-oriented attitude, strong English communication skills, and the ability to work in an interdisciplinary team are essential.

Benefits

• Pension scheme
• Paid pregnancy and maternity leave
• Partially paid parental leave
• Holiday allowance
• Year-end bonus
• Generous leave options
• On-campus children's day care
• Student sports center access
• Mental health support
• Commuting allowance
• Working from home allowance
• Internet costs allowance
• Tax compensation scheme