PostDoc in AI-enhanced solvent-based recycling of polymers

This project will be executed together with company Exergy within the EKKO Circular Plastics project. The PD researcher will develop and validate an AI-enhanced, solvent-based recycling process for complex plastic waste streams such as multilayer packaging and e-waste, while Exergy will develop the digital-twin and machine-learning tools that make the process adaptive and scalable. The research effort will be directed towards the selective recovery in high yield of high-value polymers (e.g., PET, PS, PC, ABS) at high purity, while minimizing solvent consumption and energy demand. The research activities will include understanding of performance profile of recycled polymers by means of various chemical, structural, rheological and mechanical characterization techniques.

The PD research will be carried out under the supervision of Prof. Željko Tomović in the Polymer Performance Materials group in the Department of Chemical Engineering and Chemistry at the TU/e, which is also the part of the interdisciplinary Institute for Complex Molecular Systems (ICMS), in which all polymer-related research groups at the TU/e participate. A description of the group can be found here: https://www.tue.nl/en/research/research-groups/polymer-performance-materials.

The TU/e offers academic education that is driven by fundamental and applied research. We combine scientific curiosity with a hands-on mentality. Our educational philosophy is based on personal attention and room for individual ambitions and talents. Our research meets the highest international standards of quality. We push the limits of science, which puts us at the forefront of rapidly emerging areas of research.

PostDoc in AI-enhanced solvent-based recycling of polymers

Introduction
In polymeric materials, a fundamental transition is required from synthesis out of fossil-based and one time use to continuous reuse of polymeric products. Current recycling methods cannot recover high-value polymers from complex waste streams such as multilayer packaging and mixed plastic waste, leading to large volumes being incinerated or landfilled. Solvent based recycling offers a promising route to produce high-quality recyclates, but current implementations rely on static process design and control that don’t account for feedstock variability, inefficient solvent use, and high energy demand, hindering scale-up.

Job Description
This project will be executed together with company Exergy within the EKKO Circular Plastics project. The PD researcher will develop and validate an AI-enhanced, solvent-based recycling process for complex plastic waste streams such as multilayer packaging and e-waste, while Exergy will develop the digital-twin and machine-learning tools that make the process adaptive and scalable. The research effort will be directed towards the selective recovery in high yield of high-value polymers (e.g., PET, PS, PC, ABS) at high purity, while minimizing solvent consumption and energy demand. The research activities will include understanding of performance profile of recycled polymers by means of various chemical, structural, rheological and mechanical characterization techniques.

The PD research will be carried out under the supervision of Prof. Željko Tomović in the Polymer Performance Materials group in the Department of Chemical Engineering and Chemistry at the TU/e, which is also the part of the interdisciplinary Institute for Complex Molecular Systems (ICMS), in which all polymer-related research groups at the TU/e participate. A description of the group can be found here: https://www.tue.nl/en/research/research-groups/polymer-performance-materials.

The TU/e offers academic education that is driven by fundamental and applied research. We combine scientific curiosity with a hands-on mentality. Our educational philosophy is based on personal attention and room for individual ambitions and talents. Our research meets the highest international standards of quality. We push the limits of science, which puts us at the forefront of rapidly emerging areas of research.

Job Requirements

• We seek highly talented, motivated, and enthusiastic candidates with an PhD degree in polymer chemistry, organic chemistry, or a related discipline.
• The successful candidate has a solid background in polymer and organic chemistry.
• Experience with the synthesis and characterization of polymers is essential.
• Analytical skills, initiative and creativity are highly desired.
• Excellent communication (oral and writing) skills in English (prerequisite).
• You are a naturally curious person, eager to learn more and with a strong interest in science and research.
• You should have a scientific attitude combined with a hands-on mentality and interest for application-oriented research.
• We prefer candidates with a good team spirit, who like to work in an internationally oriented, social environment.

Interviews, a scientific presentation and a small writing assignment will be part of the selection procedure.

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 12 months.
• Salary in accordance with the Collective Labour Agreement for Dutch Universities, scale 10 (min. € 4,241 max. € 5,538 gross per month).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs on general skills, didactics and topics related to research and valorization.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• Unlimited access to the modern on‑campus TU/e Student Sports Center at an exceptionally affordable rate.
• Partially paid parental leave and an allowance for commuting, working from home and internet costs.
• A TU/e Postdoc Association that helps you to build a stronger and broader academic and personal network, and offers tailored support, training and workshops.
• A Staff Immigration Team is available for international candidates, as are a tax compensation scheme (the 30% facility) and a compensation for moving expenses.

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 Chemical Engineering and Chemistry bridges science and engineering to create sustainable solutions for society. By combining expertise in molecules, materials, and processes, we drive innovations that address global challenges in energy, circularity, health and future technology. Through world-class research, interdisciplinary collaboration, and strong industry partnerships, we educate engineers and scientists who turn scientific discoveries into technologies with lasting societal impact. Together, we create chemistry for a better world.

Information
Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager Prof. Željko Tomović, z.tomovic@tue.nl.

Visit our website for more information about the application process. You can also contact Ms. Funda Gormus, HR-advisor, f.gormus@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 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.
• List of up to five self-selected ‘best publications’.

Ensure that you submit all the requested application documents. Please note that incomplete applications may not be considered and could be rejected.

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 Adapting Transformer Models for Defect Detection with Limited Data

Introduction
Join the NWO Perspectief FIND program and develop methods to adapt Transformer-based foundation models for defect detection where data is scarce and unlabeled. Explore few-shot learning, self-supervised adaptation, and synthetic data generation to enable robust, scalable AI in semiconductor and printing systems. Work with leading industry partners like Canon and help transform quality inspection in next-generation high-tech equipment!

Job Description
Industrial edge deployments—in semiconductor manufacturing, industrial printing systems, automotive radar, smart mobility cameras, and HealthTech—require on-device AI to ensure low latency, privacy, and resilience. Today’s 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 high‑tech industry, with strong emphasis on edge deployment, privacy, and timely decision‑making. Partners include ASML, NXP, Canon Production Printing, 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 adapting and fine-tuning Transformer-based foundation models for defect detection in high-tech manufacturing environments where only limited and largely unlabeled defect data is available. Current solutions typically rely on supervised CNN-based models trained on large labeled datasets, which fail when defects are rare, vary across machines, or when labeling is prohibitively expensive. These approaches lack flexibility and generalization, making them unsuitable for dynamic industrial settings with scarce and imbalanced data.

You will also explore few-shot learning, self-supervised adaptation, and multimodal integration techniques to overcome data scarcity and improve robustness. Unlike existing methods that depend on exhaustive annotation or handcrafted features, this research will leverage the rich representations of foundation models and develop strategies for zero-shot or few-shot adaptation. You will investigate domain adaptation, synthetic data generation, and cross-modal learning to enable models that generalize across defect types and machine configurations. This ensures scalable, accurate defect detection even in low-resource industrial contexts.

The resulting models will be validated in collaboration with a lead high-tech company, demonstrating how foundation models can transform quality inspection by reducing dependency on labeled data and enabling rapid adaptation to new defect patterns—closing the gap between AI capability and real-world manufacturing constraints.

Research group and company
This position is embedded in the Mobile Perception Systems (MPS) Lab and Electronic Systems (ES) group within the Electrical Engineering department at Eindhoven University of Technology (TU/e). The MPS lab and ES group have a strong history of collaborative research projects leading to real-world impact.

This PhD project is executed in close collaboration with Canon Production Printing which is a global leader in high-end digital printing, offering advanced hardware, software, and services aimed at professional and industrial-scale print environments.

Job Requirements

• A master’s degree (or an equivalent university degree) in Computer Science, Electrical Engineering, Artificial Intelligence, or related background.
• Strong background in machine learning, computer vision, and deep learning.
• Knowledge of transformer architectures and foundation models.
• Experience with few-shot learning, self-supervised learning, or domain adaptation is a plus.
• Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow).
• 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 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.
• Unlimited access to the modern on‑campus TU/e Student Sports Center at an exceptionally affordable rate.
• 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.

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 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 dr.ir. Sander Stuijk, s.stuijk@tue.nl.

Visit our website for more information about the application process. You can also contact Kevin Caris, HR advisor, k.t.caris@tue.nl or +31 40 247 8835.

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 Adapting Transformer Models for Defect Detection with Limited Data

Industrial edge deployments—in semiconductor manufacturing, industrial printing systems, automotive radar, smart mobility cameras, and HealthTech—require on-device AI to ensure low latency, privacy, and resilience. Today’s 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 high‑tech industry, with strong emphasis on edge deployment, privacy, and timely decision‑making. Partners include ASML, NXP, Canon Production Printing, 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 adapting and fine-tuning Transformer-based foundation models for defect detection in high-tech manufacturing environments where only limited and largely unlabeled defect data is available. Current solutions typically rely on supervised CNN-based models trained on large labeled datasets, which fail when defects are rare, vary across machines, or when labeling is prohibitively expensive. These approaches lack flexibility and generalization, making them unsuitable for dynamic industrial settings with scarce and imbalanced data.

You will also explore few-shot learning, self-supervised adaptation, and multimodal integration techniques to overcome data scarcity and improve robustness. Unlike existing methods that depend on exhaustive annotation or handcrafted features, this research will leverage the rich representations of foundation models and develop strategies for zero-shot or few-shot adaptation. You will investigate domain adaptation, synthetic data generation, and cross-modal learning to enable models that generalize across defect types and machine configurations. This ensures scalable, accurate defect detection even in low-resource industrial contexts.

The resulting models will be validated in collaboration with a lead high-tech company, demonstrating how foundation models can transform quality inspection by reducing dependency on labeled data and enabling rapid adaptation to new defect patterns—closing the gap between AI capability and real-world manufacturing constraints.

Research group and company
This position is embedded in the Mobile Perception Systems (MPS) Lab and Electronic Systems (ES) group within the Electrical Engineering department at Eindhoven University of Technology (TU/e). The MPS lab and ES group have a strong history of collaborative research projects leading to real-world impact.

This PhD project is executed in close collaboration with Canon Production Printing which is a global leader in high-end digital printing, offering advanced hardware, software, and services aimed at professional and industrial-scale print environments.

Teacher Education Specialist

Job Description
About the TEACH team and job description
The TEACH team consists of seven teacher education specialists and facilitates the professional development of all teaching staff at the TU/e (from student tutors to professor). The TEACH team is part of HR Development and strives to strengthen the educational quality at the TU/e with their offer. We do this by designing, developing, and giving training or other professionalization activities, and advising on teacher professionalization. In doing so, we remain in close contact with the nine different departments at the TU/e and their learning needs. The implementation, the supervision during, and the further development of the University Teaching Qualification (UTQ, Basis Kwalificatie Onderwijs in Dutch) program is a substantial part of our work. Besides that, we provide workshops alongside the UTQ program and are developing a program that will further support teachers in their educational career path after the UTQ program (continuous professional development). In general, topics that we focus on are designing and implementing student-centered & active learning, assessment, diversity & inclusion, and Gen AI in education.

As a teacher education specialist, you have a great passion for learning and the development of our academic teaching staff. Your job consists of four components:

• You will (re)design training programs, and other professionalization activities and learning interventions (e.g., e-modules and knowledge clips), within the team or in collaboration with internal and external stakeholders.
• You will implement and execute trainings and workshops on education-related topics (including UTQ).
• You will execute trainings for students who will become a tutor.
• You coordinate the organization of the education-related training (programs) and maintain contact with external stakeholders, in order to warrant the quality of these trainings.
• You will provide input for the elaboration of policy components in the field of teacher professionalization and establish and maintain contact with stakeholders within the departments and services (such as educational directors, Educational advisor-central and -departmental) with the aim to translate new professional development needs into learning interventions (e.g., courses, coaching, learning communities). You will also advise employees and supervisors about the development possibilities within the teacher professionalization offer.

Job Requirements
In order to be successful in this role we are looking for a candidate with:

• knowledge and a bachelor’s or master's degree, in the field of educational sciences or similar;
• knowledge and experience with the design and execution of trainings and other learning interventions in higher education (e.g., assessment, course design, teaching skills and supervising students). Experience with developing e-learning in Articulate Rise 360 is a plus;
• expertise in GenAI in education;
• strong advisory skills / communication skills / analytical skills;
• the ability to effectively collaborate in a team that values alignment within the team;
• expertise in blended learning and experience with learning management systems (for instance Canvas, Rise up);
• preferably experience in higher education;
• good command of the English language (B2 level) is essential and good command of the Dutch language is preferable.

Conditions of Employment
An exciting position within an international yet personal university. You are right in the middle of the students, on a green campus within walking distance of the central station. Besides beautiful architecture, you will find varied workplaces and excellent sports facilities. We also offer you:

• A one-year contract upon starting, with the possibility of renewal for another 2 years.
• A monthly salary of minimum € 4.728,- to maximum € 6.433,- for full-time employment, depending on your knowledge and experience (scale 11 Collective Labour Agreement for Dutch Universities).
• In addition to vacation pay, a structural end-of-year bonus of 8.3%.
• A favorable arrangement for more holidays or a sabbatical.
• A selection model for additional fringe benefits.
• Working hours in consultation for an optimal work-life balance.
• Scope for your talent with advancement prospects and excellent development opportunities such as mentoring, workshops and coaching.
• Partially paid parental leave and reimbursement for commuting expenses, working from home and the internet.
• A generous employer contribution to the favorable ABP pension plan.
• Unlimited access to the modern on‑campus TU/e Student Sports Center at an exceptionally affordable rate.

Here 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.

Human Resource Management
The HRM department at the TU/e is an enabler in attracting and developing talent for the university aligned with the TU/e mission, vision and strategy. We have a large team of over 100 HR professionals in HR Talent Development, HR Business and HR Operations. HR Talent Development focuses on all programs around employee development from onboarding, to teaching, training and leadership development. The HR Business team has HR advisors working with leaders in our departments as well as the Talent Acquisition team responsible for recruitment. And the HR Operations team is the backbone of our HRM organization with professionals focusing on Compensation & Benefits, Payroll, HR Services and administration, International Hires, Data & Insights and Processes & Systems.

Information
Information about the content of the position is available from Rachelle Kamp, team leader TEACH (r.j.a.kamp@tue.nl, 06-53653812), or Rob van der van der Linden, internal Recruiter ((r.m.e.v.d.linden@tue.nl).

Application
If you are interested, please use the ‘apply’ button to send us your CV and letter of application with your motivation and a description of your qualifications. Ensure that you submit all the requested application documents. We give priority to complete applications.

Screening of candidates begins as soon as applications are received and continues until the position is filled. Where applicable, internal candidates will be given priority over external candidates where they are equally suitable.

We look forward to receiving your application!

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.
• Important for non-EU applicants: Please be aware that for this position, specific residence permit requirements apply. If you are a non-EU national, you may not be eligible to legally work in this role under current Dutch immigration regulations. We strongly advise you to contact our Staff Immigration Team (staffimmigration@tue.nl) before applying to check your eligibility and to receive further guidance.
• Please do not contact us for unsolicited services.