PhD on combined physics- and machine learning-based modeling of complex dynamical systems

Introduction
Are you passionate about the modelling of complex dynamical systems using both physics-based knowledge and machine learning? Are you interested in synergizing these techniques to construct models with superior predictive capacity? Are you eager to apply and valorize scientific results in this field in high-tech domains such as semiconductor machines, together with a highly innovative company? Would you like to work in a team of 2 PhD students? Then, this PhD position is made for you!

Job Description
We invite highly motivated students with a strong background in dynamical systems, machine learning, and mathematical system theory to apply this PhD position within the Dynamics and Control section at the Department of Mechanical Engineering, Eindhoven University of Technology. The mission of the Dynamics and Control Section is to perform research and train next-generation students on the topic of understanding and predicting the dynamics of complex engineering systems in order to develop advanced control, estimation, planning, and learning strategies which are at the core of the intelligent autonomous systems of the future: Designing and realizing smart autonomous systems for industry and society.

The design and operation of complex high-tech systems, such as semiconductor equipment requires the construction of highly accurate (multi-physics) models to predict their behaviour. While physics-based models are still used in practice, they lack the extreme accuracy required due to unavoidable model mismatch (especially in a multi-physics context). In contrast, AI and machine learning can potentially help to construct highly accurate models; however, such models typically lack interpretability, and generalizability beyond the training dataset. This project aims to synergize both approaches in a hybrid modelling framework. As a core industrial use case, we will consider the semiconductor equipment for heterogeneous integration (hybrid bonding) of ASMPT (https://www.asmpt.com/).

Within this project, in which 2 PhD students will be employed at the Eindhoven University of Technology, you will develop novel tools for such hybrid modeling to generate highly predictive dynamical models. This will help to design and operate the semiconductor equipment of the future. This position will help you to build both a strong academic and industrial research profile.

You will have access to the graduate courses at the Dutch Institute of Systems and Control (DISC) and the Engineering Mechanics Research School (EM), and will have the opportunity to collaborate with industry in the Brainport region and academic researchers worldwide. By joining us, you will be part of a vibrant community of more than 60 researchers including faculty members, postdocs and PhDs working on diverse topics in the field of dynamical systems and control and its applications.

This PhD position is jointly supervised by Nathan van de Wouw.

Job Requirements

• A master’s degree (or an equivalent university degree) in dynamical systems, mechanical engineering, electrical engineering, AI and machine learning or applied mathematics.
• Strong background in dynamical systems, systems theory, and machine learning.
• Affinity with application domains within mechanical engineering such as mechatronics or robotics.
• Knowledge of at least one programming language: Matlab, Python, is expected.
• Experience and/or a keen interest in the field of dynamical systems, and machine learning.
• Eager to work within a team and independently.
• Ability to collaborate with industry and academic researchers.
• Fluent in spoken and written English.

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. € 3,059 max. € 3,881).
• 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 children's 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 13,000 students and more than 7,000 staff, forming a diverse and vibrant academic community.

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.

Information
Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager prof.dr.ir. Nathan van de Wouw, N.v.d.Wouw@tue.nl, https://vandewouw.dc.tue.nl/. IMPORTANT: always put Mrs. Geertje Janssen-Dols in cc in all your communication (G.Janssen-Dols@tue.nl).

Visit our website for more information about the application process or the conditions of employment. You can also contact HR Services Gemini, HRServices.Gemini@tue.nl or HR Advice, HRAdviceME@tue.nl.

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 now'-button on this page. The application should include the following documents as a single PDF file:

• 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.
• An explanation of your interest in the proposed research topic.
• Copies of diplomas and academic transcripts with grades from prior university studies.
• Brief description of your MSc thesis and/or relevant research projects.

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.
• Due to the Christmas period, it may take longer than usual to receive a response from us.
• Please do not contact us for unsolicited services.

PhD on combined physics- and machine learning-based modeling of complex dynamical systems

We invite highly motivated students with a strong background in dynamical systems, machine learning, and mathematical system theory to apply this PhD position within the Dynamics and Control section at the Department of Mechanical Engineering, Eindhoven University of Technology. The mission of the Dynamics and Control Section is to perform research and train next-generation students on the topic of understanding and predicting the dynamics of complex engineering systems in order to develop advanced control, estimation, planning, and learning strategies which are at the core of the intelligent autonomous systems of the future: Designing and realizing smart autonomous systems for industry and society.

The design and operation of complex high-tech systems, such as semiconductor equipment requires the construction of highly accurate (multi-physics) models to predict their behaviour. While physics-based models are still used in practice, they lack the extreme accuracy required due to unavoidable model mismatch (especially in a multi-physics context). In contrast, AI and machine learning can potentially help to construct highly accurate models; however, such models typically lack interpretability, and generalizability beyond the training dataset. This project aims to synergize both approaches in a hybrid modelling framework. As a core industrial use case, we will consider the semiconductor equipment for heterogeneous integration (hybrid bonding) of ASMPT (https://www.asmpt.com/).

Within this project, in which 2 PhD students will be employed at the Eindhoven University of Technology, you will develop novel tools for such hybrid modeling to generate highly predictive dynamical models. This will help to design and operate the semiconductor equipment of the future. This position will help you to build both a strong academic and industrial research profile.

You will have access to the graduate courses at the Dutch Institute of Systems and Control (DISC) and the Engineering Mechanics Research School (EM), and will have the opportunity to collaborate with industry in the Brainport region and academic researchers worldwide. By joining us, you will be part of a vibrant community of more than 60 researchers including faculty members, postdocs and PhDs working on diverse topics in the field of dynamical systems and control and its applications.

This PhD position is jointly supervised by Nathan van de Wouw.

PhD on develop an agent-based digital twin platform for collective energy transition decisions

Introduction
Are you passionate about leveraging AI and digital tools for sustainability transitions and developing decision support systems to accelerate collective energy transition? Join our team to drive innovations that support urban resilience. This is your chance to make a real impact!

Job Description
The energy and the climate crisis reinforced the urgency of energy efficiency in residential buildings in Europe. To meet its climate targets, the Netherlands needs to decarbonize the residential sector. This requires that many distributed actors (households, owners associations, social housing associations, and citizen collectives) invest in energy transition rapidly and at scale. Yet citizens’ collective (housing associations, homeowners' associations, etc.) investments in energy-related home renovation will impact integrated energy systems, including correlations between energy grids, uncertainties and simultaneous peak loads. In this project, Digital pathways for accelerating collective decision making of energy renovation in the built environment (DPARt): Through data-driven and user-centric design approach for VvEs, we explore a promising solution.

Sustainability, in its broadest definition, is the cornerstone of research and education in the Department of Built Environment. We take the lead in (re)shaping the built environment to be future-proof, safe, healthy, inclusive and respectful of planetary boundaries. We house the entire spectrum of technology, engineering, design, and human behavior disciplines in the built environment, with world-class experimental facilities at all scales. This allows us to address societal challenges from a uniquely integrated perspective.

Job responsibilities

• Transforms floorplans and construction drawings of buildings into geo-data, and generate potential energy efficiency improvement options, and visualize for end users.
• Develop an agent-based digital twin platform of VvEs to support the energy transition decision making.
• Further develop agent-based model for collective decision-making process addressing uncertainties and validating in living labs.
• Design narrative dynamics for serious games incorporating AI agents for effective interactive communication.
• Publishing articles in high-quality scientific journals.
• Coordination of research activities.
• You will be contributing to the Urban Informatics research theme at the chair of Information Systems in the Built Environment (ISBE).

Job Requirements

• A master’s degree (or an equivalent university degree) in Building engineer or Environmental Science, Planning, Engineering, computer science, innovation science or related fields with demonstrable experience in spatial analysis and numerical modelling of the environments.
• A research-oriented attitude.
• Solid programming knowledge (Python, and preferably web development languages), and some experience with ABM or willing to learn ABM is a plus
• A good understanding of AI techniques, especially open for new challenges.
• Ability to work in an interdisciplinary team and interested in collaborating with societal partners.
• Motivated to develop your teaching skills and coach students.
• Fluent in spoken and written English (C1 level). Knowledge of Dutch language is useful to connect with societal partners and test the developed model in the living labs.
• A positive attitude and good communication skills.
• Excellent interpersonal skills, with the ability to work in a team and on own initiative.

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. € 3,059 - max. € 3,881).
• 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 children's 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 13,000 students and more than 7,000 staff, forming a diverse and vibrant academic community.

Information
Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager, dr. Qi Han, q.han@tue.nl or dr. Dujuan Yang, D.Yang@tue.nl.

Visit our website for more information about the application process or the conditions of employment. You can also contact Leo van Houten, HR-Advisor, l.v.houten@tue.nl or +31 40 247 8269.

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.

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 Immunomodulation for In Situ Tissue Regeneration

Introduction

• Are you inspired by the potential to use degradable synthetic implants that are transformed into living tissues by the body itself?
• Are you fascinated by our ability to use the immune system to our advantage to induce tissue regeneration in situ?
• Are you eager to contribute to better and sustainable healthcare?
• Are you passionate about inspiring and mentoring students and working in a high-end collaborative and interdisciplinary research environment?
• Are you our next PhD in Immunomodulation for Tissue Regeneration?

Job Description
There is an increasing clinical demand for sophisticated medical implants and the scientific field of implant technology is exponentially growing. The main challenge is to harness the immune response to such an implant. In this research we use the immune response to our advantage, by using bioresorbable synthetic biomaterials that are gradually replaced by living tissue, directly at the functional site in the body, or in situ. Our research on this technology has, for example, led to the world’s first clinical trials using regenerative heart valves for children with congenital cardiac malformations.

While tremendous progress has been made in terms of manufacturing technologies for biomaterials and implants, there is relatively little progress in terms of fundamentally understanding the inflammatory and regenerative process in vivo. The various cellular interactions in in situ tissue engineering, and how these are influenced by (1) the local niche (i.e. biomaterial and extracellular matrix environment) and (2) systemic factors (e.g. immunological state), are largely overlooked to date. Moreover, preclinical in vivo studies have yielded unexplained variabilities in outcome. This raises the question to what extent patient-specific systemic aspects influence the local immunological and regenerative processes. The main aim of your research will be to delineate the combined influences of systemic immunity and local immunomodulation on the process of in situ tissue regeneration at the local tissue site, in order to (i) enable the elucidation of patient-specific immunity on in situ tissue regeneration, and (ii) enable the (precision) targeting of systemic immunity to steer the regenerative process at any time during implantation.

Embedding
You will be embedded in a highly inspiring research environment, both socially and professionally, which facilitates access to high-end research facilities, as well as fosters interdisciplinary collaborations. Your project is part of the DRIVE-RM research program (NWO Summit) and will run in close collaboration with partners from University Medical Center Utrecht. You will be an integral member of the ImmunoRegeneration team and the overarching Soft Tissue Engineering and Mechanobiology research group (headed by Prof. Carlijn Bouten) at our Department. Moreover, you will be affiliated to the Institute for Complex Molecular Systems, our interdepartmental center for research excellence.

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.

Job Requirements

• A master’s degree (or an equivalent university degree) in Biomedical Engineering.
• A research-oriented attitude.
• Experience in cell culture is a must. Expertise/training in immunology and/or tissue regeneration is strongly preferred.
• Ability to work in a multidisciplinary team with researchers at different academic levels (MSc, PhD, Post-doc).
• 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. € 3,059 - max. € 3,881 gross per month).
• 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 children's 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 13,000 students and more than 7,000 staff, forming a diverse and vibrant academic community.

Information
Do you recognize yourself in this profile and would you like to know more? Please contact Anthal Smits, Associate Professor of ImmunoRegeneration, a.i.p.m.smits@tue.nl.

Visit our website for more information about the application process or the conditions of employment. You can also contact Sascha Sanchez, HR advisor, s.j.m.g.sanchez.van.oort@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.

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 on develop an agent-based digital twin platform for collective energy transition decisions

The energy and the climate crisis reinforced the urgency of energy efficiency in residential buildings in Europe. To meet its climate targets, the Netherlands needs to decarbonize the residential sector. This requires that many distributed actors (households, owners associations, social housing associations, and citizen collectives) invest in energy transition rapidly and at scale. Yet citizens’ collective (housing associations, homeowners' associations, etc.) investments in energy-related home renovation will impact integrated energy systems, including correlations between energy grids, uncertainties and simultaneous peak loads. In this project, Digital pathways for accelerating collective decision making of energy renovation in the built environment (DPARt): Through data-driven and user-centric design approach for VvEs, we explore a promising solution.

Sustainability, in its broadest definition, is the cornerstone of research and education in the Department of Built Environment. We take the lead in (re)shaping the built environment to be future-proof, safe, healthy, inclusive and respectful of planetary boundaries. We house the entire spectrum of technology, engineering, design, and human behavior disciplines in the built environment, with world-class experimental facilities at all scales. This allows us to address societal challenges from a uniquely integrated perspective.

Job responsibilities

• Transforms floorplans and construction drawings of buildings into geo-data, and generate potential energy efficiency improvement options, and visualize for end users.
• Develop an agent-based digital twin platform of VvEs to support the energy transition decision making.
• Further develop agent-based model for collective decision-making process addressing uncertainties and validating in living labs.
• Design narrative dynamics for serious games incorporating AI agents for effective interactive communication.
• Publishing articles in high-quality scientific journals.
• Coordination of research activities.
• You will be contributing to the Urban Informatics research theme at the chair of Information Systems in the Built Environment (ISBE).