PhD in Computer Architecture for Real-Time AI at the Edge

Introduction

Join the NWO Perspectief FIND program and help design next-generation computer architectures for running large AI models on embedded and edge systems under strict timing energy and memory constraints. Youll explore hardware-aware optimization and co-design with accelerators (FPGAs GPUs near-memory systems) to achieve real-time energy-efficient AI for high-tech industry applications. Work with leading companies like ASMPT and shape the future of AI at the edge!

Job Description

Industrial edge deploymentsin semiconductor manufacturing printing systems automotive radar smart mobility cameras and HealthTechrequire on-device AI to ensure low latency privacy and resilience. Todays Transformers models scale poorly and assume abundant cloud resources. The research program FIND aims to deliver architectural and algorithmic breakthroughs that enable foundation models to run predictably and efficiently on embedded processors and accelerators.

FIND is a research program funded by the Dutch government and industry that brings together 5 universities 11 companies (startups to multinationals) and 2 knowledge institutes to develop foundation models (large AI models) for Dutch hightech industry with strong emphasis on edge deployment privacy and timely decisionmaking. Partners include ASML NXP Canon ASMPT Technolution Signify Shell Stryker TNO and others. A total of 12 PhDs will be employed on the FIND program covering topics from foundation model pre-training and multimodal adaptation to architectures and compression for edge deployment while targeting real-world validation in domains like HealthTech smart industry and autonomous mobility.

This PhD position focuses on the design of novel computer architectures to enable large AI models to run on embedded and edge systems under strict timing energy and memory constraints. Current solutions typically rely on general-purpose CPUs or GPUs which are optimized for flexibility rather than predictable real-time performance. These platforms struggle with the complexity and resource demands of foundation models leading to high latency excessive energy consumption and large memory footprintsmaking them unsuitable for safety-critical or resource-constrained environments.

In addition you will explore model compression and co-optimization with hardware accelerators. Unlike existing approaches that apply compression techniques (e.g. pruning quantization) in isolation this research will jointly optimize algorithms and -optimization ensures that architectural decisionssuch as token merging sparsity exploitation and quantizationare aligned with hardware datapaths and memory hierarchies. This enables predictable real-time performance energy efficiency and scalability across heterogeneous platforms (embedded CPUs GPUs FPGAs near-memory accelerators). The result is a shift from best-effort execution on generic hardware to deterministic deadline-compliant AI inference tailored for industrial edge systems.

These architectures and accelerators will be prototyped and validated in collaboration with a leading high-tech semiconductor manufacturing company demonstrating how edge devices can execute foundation models with guaranteed performance and minimal energy footprintclosing the gap between AI capability and embedded system constraints.

Research group and company

This position is embedded in the Electronic Systems (ES) group within the Electrical Engineering department at Eindhoven University of Technology (TU/e). Our group consists of six full professors three associate professors seven assistant professors several postdocs approximately 40 EngD and PhD candidates and support staff. The ES group is world-renowned for its design automation and embedded systems research.

This PhD project is executed in close collaboration with ASMPT which is the only company in the world offering a comprehensive portfolio of integrated solutions for all major steps in electronics manufacturing from carrier for chip interconnection to chip assembly and packaging to SMT.

Job Requirements

• A masters degree (or an equivalent university degree)in Computer Engineering Electrical Engineering Embedded Systems or related background and strong hardware design skills.
• Strong background in computer architecture digital design and real-time systems.
• Knowledge of embedded systems and hardware/software co-design.
• Experience with hardware acceleration (FPGAs GPUs SoCs) and low-power design.
• Familiarity with deep learning frameworks (e.g. PyTorch TensorFlow) is a plus.
• Ability to work in an interdisciplinary team.
• 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 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. 3059 - max. 3881).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• 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 on-campus childrens day care and sports facilities.
• An allowance for commuting working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.

About us

Eindhoven University of Technology is a leading international university within the Brainport region 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 13000 students and more than 7000 staff forming a diverse and vibrant academic community.

The mission of the Department of Electrical Engineering is to acquire share and transfer knowledge and understanding in the whole field of Electrical Engineering through education research and valorization. We work towards a Smart Sustainable Society a Connected World and a healthy humanity (Care & Cure). Activities share an application-oriented character a high degree of complexity and a large synergy between multiple facets of the field.

Research is carried out into the applications of electromagnetic phenomena in all forms of energy conversion telecommunication and electrical signal processing. Existing and new electrical components and systems are analyzed designed and built. The Electrical Engineering department takes its inspiration from contacts with high-tech industry in the direct surrounding region and beyond.

The department is innovative and has international ambitions and partnerships. The result is a challenging and inspiring setting in which socially relevant issues are addressed.

Information

Do you recognize yourself in this profile and would you like to know more Please contact the hiring manager . Sander Stuijk .

Visit our website for more information about the application process or the conditions of employment. You can also contactKevin Caris HR advisor or.

Curious to hear more about what its 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.