Semiconductor industry we are looking for 6 PhDs

The Department of Mechanical Engineering department conducts world-class research aligned with the technological interests of the high-tech industry in the Netherlands with a focus on the Brainport region. Our goal is to produce engineers who are both scientifically educated and application-driven by providing a balanced education and research program that combines fundamental and application aspects. We equip our graduates with practical and theoretical expertise preparing them optimally for future challenges.

Introduction

Do you like applying mathematical theories in practice to solve real-world challenges Do you like working with top-notch internationally recognized industrial partners Would you like to push the boundaries of system-level modelling analysis design exploration and synthesis beyond the current state-of-the-art Or are you curious to learn more about the application of AI for system diagnostics and system evolution We are offering six PhDs positions across the department of Mechanical Engineering Electrical Engineering and Mathematics and Computer Science that focus on solving cutting-edge design automation questions that will drive innovation in the Brainport region and beyond.

Job Description

The semiconductor industry is vital to global economic growth and technological progress powering everything from energy solutions to healthcare innovations. Some parts of the design of these high-tech systems - lithography machines can be automated. However current design automation solutions are limited to specific domains or subsystems. Holistic system-level design is a necessary next-step because of the complexity of these systems. This implies going beyond current domain-specific solutions by combining mechanical electrical software data science and AI-expertise.

We are seeking highly-motivated candidates for the six PhD positions advertised here that address the challenge above by pushing the boundaries of 1) system-level co-design of architecture functionality and performance and 2) leverage system models to monitor and understand operational behaviour of systems and act upon irregularities. This description will continue with a description of all six positions:

PhD 1: System architecture exploration (supervision by: Theo Hofman and Pascal Etman) Department of Mechanical Engineering

The research by this position aims to achieve automated computational design synthesis of system topologies. This will result in new and novel system topologies which can be automatically analysed and evaluated based on construction and performance indicators. This will require an insight into how to fully automate the discrete topology design synthesis process across system levels and how to learn component descriptions and how these can be interconnected.

PhD 2: Scenario-based compositional system-level performance engineering (supervision by: Twan Basten Marc Geilen) Department of Electrical Engineering

The research planned for this position will explore how to achieve correct behavior and required performance (in terms of throughput latency) while considering concerns and constraints from various engineering domains by leveraging scenario-based design. This methodology considers systems from a behavioral perspective clustering behaviors with similar characteristics. The envisioned model-based performance engineering starts from use cases (typical and exceptional) and various system scenarios (different operating modes failures). This will require the development of suitable domain-specific languages (DSLs) to specify use cases from the perspective of the relevant domains as well as model transformation analysis and synthesis algorithms that provide functionally correct system configurations with a guaranteed performance. The research focuses on suitable domain models and the required model transformation analysis and synthesis approaches.

We require candidates for PhD 2 to have good software engineering and programming skills good knowledge on modeling cyber-physical systems and preferably experience with DSL development.

PhD 3: Timing-aware distributed supervisory controller synthesis (supervision by: Michel Reniers and Martijn Goorden) Department of Mechanical Engineering

The automatic synthesis of supervisory controllers will support the design process of lithography machines. To achieve a computationally tractable automatic synthesis a deep understanding is required into how the (control) architecture of lithography machines and the types of models can be used to capture their behaviour and requirements. This will enable splitting the problem of synthesizing safe supervisors into smaller synthesis problems the results of which can be composed into a solution for the original system. This will require an understanding of how existing system decompositions and interface specifications be translated into models suitable for hierarchical/distributed supervisory control synthesis.

We require candidates for PhD 3 to have knowledge of discrete-event systems and knowledge of or interest in learning about formal methods in particular the theory of Supervisory Controller Synthesis.

PhD 4: AI-driven legacy system explanation and refactoring (supervision by: Lina Ochoa Venegas Michel Chaudron Jacob Krüger - collaboration with: Stef van den Elzen) Department of Mathematics and Computer Science

Maintaining an understanding of complex systems with a vast amount of software requires effective architecture explanation frameworks visualizations and identification of architectural refactoring opportunities in legacy systems. These elements will support onboarding enhance team efficiency reduce reliance on system experts and accelerate software maintenance and development. This will require an understanding of how a system architecture can be extracted and represented in a language-agnostic way relying in the capabilities of program analysisgenerative AI and advanced visualisation techniques.

We require candidates for PhD 4 to hold both a bachelors and a masters degree in Computer Science Data Science Software Engineering or a closely related field. Prior experience with the application and understanding of generative AI methods and tools as well as a solid background in software engineering and a strong interest on visualisation techniques are highly desirable.

PhD 5: Data mining for diagnostics (supervision by: Mykola Pechenizkiy Songgaojun (Amy) Deng) Department of Mathematics and Computer Science

This research in close collaboration with ASML AI Research is based on the promise of a novel framework that integrates evolving knowledge graphs (KGs) with domain-specific foundation models to enhance diagnostic capabilities. This research will look into how knowledge graphs be designed and generated from a combination of software stack legacy code and documentation (V-model steps: requirements system design module design etc) to capture equipment behavior process steps and causal failure relations as well as discrepancies between expected behavior from requirements and actual failures from deployed system. The latter knowledge graphs will then be integrated with foundation models to enhance machine interpretability in complex diagnostic environments.

PhD 6: Automating health monitoring in semicon equipment (supervision by: Nathan van de Wouw Tom Oomen Michelle Chong) Department of Mechanical Engineering

This project aims to devise innovative monitoring technology for high-tech systems to perform (semi) autonomous fault isolation and predictive monitoring. For high-tech equipment such as those used in the production of semiconductors monitoring is a challenge because of the highly complex interconnected systems they are consisting of many components/modules which are functionally digitally and physically connected. To achieve such monitoring technology an understanding is required of how to support the identification and prediction of rare failures for which only scarce datasets are available.

Current health monitoring technology is typically designed for either (i) individual system components/modules (which would require removing the component from the system which is not possible in practice) or (ii) for the system as a whole. The first approach fails to account for the interaction between modules and its effect on the whole system. The second approach makes it challenging to isolate which module may be failing and how to zoom in on the part of the system that is the root cause of the failure. This PhD position will address this challenge by developing hierarchical diagnostic tools for complex dynamical systems.

We require candidates for PhD 6 to have a strong background in mathematical control theory and a keen interest in hybrid dynamical systems observer design learning techniques and optimization.

Job Requirements

• A masters degree (or an equivalent university degree)in systems and control mechanical engineering electrical engineering mathematics and/or computer science data science or applied mathematics. (depending on the position applied for)
• Strong analytical and problem-solving skills with a passion for tackling complex multi-disciplinary challenges.
• Eager to work within an interdisciplinary team of industry and academic researchers.
• Excellent communication and collaboration skills to work effectively within a large project team.

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.

A challenging and rewarding PhD position in a vibrant research environment at the forefront of semiconductor technology. The opportunity to work on a high-impact project with strong industry and academic partners. A comprehensive training program to enhance your research and professional 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.

Information

Do you recognize yourself in this profile and would you like to know more Please contact
the supervisors as indicated in the PhD position using the contact details below:

PhD1: Theo Hofman
PhD2: . Twan Basten
PhD3: . M.A. Reniers
PhD4: Dr. Lina Ochoa Venegas
PhD5: . Mykola Pechenizkiy
PhD6: . Nathan van de Wouw

Visit our website for more information about the application process or the conditions of employment. You can also contact HR Services Gemini ()or HR Advice ().

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.
• If you are interested in applying for multiple positions clearly indicate in your cover letter the order of preference and why you have chosen this specific order.

We look forward to your application and will screen it as soon as we have received it. Screening will continue until the positions have been 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 Chapter 8 of the National Knowledge Security Guidelines.
• Please do not contact us for unsolicited services.

Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow.