PhD in Visual Analytics for Historical Paintings

Introduction
We are seeking a motivated PhD candidate to develop innovative visual analytics methods for the study of historical paintings in an interdisciplinary research environment. You will contribute to the development of state‑of‑the‑art methods that advance the analysis of complex, multi‑dimensional imaging data and enable richer exploration of historical paintings in collaboration with the Van Gogh Museum.

Job Description
Paintings are examined to understand artistic processes, support conservation, and aid authentication. Modern scientific imaging techniques now generate large volumes of complex data that can reveal new insights for art historians, conservators, and the broader public. Among these techniques, X-ray fluorescence (XRF) imaging provides detailed information on the distribution of chemical elements across paint layers, giving unique access to pigments, mixtures, and underlying compositions. The resulting datasets are high‑dimensional and spatially rich, posing significant analytical challenges.

The generation of the high-dimensional images themselves follows a complex pre-processing from the raw scans usually to chemical components. Information is lost and uncertainty is added in the process.

Visual analytics offers promising ways to interpret such high‑dimensional imaging (HDI) data, yet dedicated methodologies tailored to paintings remain underdeveloped. This PhD project aims to create interactive visual analysis frameworks and methods that enable reliable, interpretable exploration of multimodal painting data, with a strong focus on XRF‑derived HDI. The project will deliver new visual analytics techniques and prototype tools that support the study of pictorial artworks.

It is expected that the candidate will author high-quality scientific papers and showcase outputs of this work at international conference. The candidate will also implement open-source software prototypes to demonstrate the effectiveness of the proposed methods.

This position is part of a collaboration between the Visualization Cluster (https://research.tue.nl/en/organisations/visualization-3/) at Eindhoven University of Technology (TU/e) and the Van Gogh Museum. TU/e provides leading expertise in visual analytics, scientific visualization, Explainable AI and the analysis of complex imaging datasets. It has generated several award winning contributions at major visualization conferences (IEEE VIS, IEEE InfoVis, IEEE VAST, EuroVis). The Van Gogh Museum, one of the world’s foremost cultural heritage institutions, increasingly acquires such data in its research on the Van Gogh collection. This partnership offers a unique opportunity to contribute to cutting‑edge digital heritage research at the intersection of science, technology, and the arts.

The project will be developed within the visualization cluster van Gogh Museum under the supervision of Prof. Anna Vilanova (a.vilanova@tue.nl), dr. Sarah Schölten, and dr. Ana Teixeira Martins (van Gogh Museum).

Job Requirements
We are looking for a candidate who meets the following requirements:

• You are enthusiastic about research in visual analytics, visualization and arts.
• You have experience with or a strong background in visualization, visual analytics, computer graphics, and/or machine learning. Preferably you finished a master in Computer Science, (Applied) Mathematics or related masters.
• Expertise in the field of visualization or visual analytics.
• You have good communication skills and are able to work in a multidisciplinary team.
• You have strong programming skills (e.g., C++, Python, …).
• You are creative, critical, analytical, motivated and persistent.
• You have a good command of the English language (knowledge of Dutch is not required).

Please note: Each project requires a different mix of skills and attitude. Please use the TU/e PhD Competence Profile to determine which competences you find most important and want to mention as a job requirement (aside from the aspects mentioned above).

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.
• 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.

As one of the largest and most dynamic academic communities in the Netherlands, the department Mathematics and Computer Science (M&CS) brings together more than 140 scientific staff, over 250 PhD and EngD candidates, and nearly 3000 students. We collaborate closely with leading industrial partners in the Brainport Eindhoven region and with universities across the globe—creating a uniquely fertile environment for both fundamental breakthroughs and applied innovation.

Information
Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager Prof. Anna Vilanova, a.vilanova@tue.nl.

Visit our website for more information about the application process. You can also contact HRServices.mcs@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.

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 Visual Analytics for Historical Paintings

Paintings are examined to understand artistic processes, support conservation, and aid authentication. Modern scientific imaging techniques now generate large volumes of complex data that can reveal new insights for art historians, conservators, and the broader public. Among these techniques, X-ray fluorescence (XRF) imaging provides detailed information on the distribution of chemical elements across paint layers, giving unique access to pigments, mixtures, and underlying compositions. The resulting datasets are high‑dimensional and spatially rich, posing significant analytical challenges.

The generation of the high-dimensional images themselves follows a complex pre-processing from the raw scans usually to chemical components. Information is lost and uncertainty is added in the process.

Visual analytics offers promising ways to interpret such high‑dimensional imaging (HDI) data, yet dedicated methodologies tailored to paintings remain underdeveloped. This PhD project aims to create interactive visual analysis frameworks and methods that enable reliable, interpretable exploration of multimodal painting data, with a strong focus on XRF‑derived HDI. The project will deliver new visual analytics techniques and prototype tools that support the study of pictorial artworks.

It is expected that the candidate will author high-quality scientific papers and showcase outputs of this work at international conference. The candidate will also implement open-source software prototypes to demonstrate the effectiveness of the proposed methods.

This position is part of a collaboration between the Visualization Cluster (https://research.tue.nl/en/organisations/visualization-3/) at Eindhoven University of Technology (TU/e) and the Van Gogh Museum. TU/e provides leading expertise in visual analytics, scientific visualization, Explainable AI and the analysis of complex imaging datasets. It has generated several award winning contributions at major visualization conferences (IEEE VIS, IEEE InfoVis, IEEE VAST, EuroVis). The Van Gogh Museum, one of the world’s foremost cultural heritage institutions, increasingly acquires such data in its research on the Van Gogh collection. This partnership offers a unique opportunity to contribute to cutting‑edge digital heritage research at the intersection of science, technology, and the arts.

The project will be developed within the visualization cluster van Gogh Museum under the supervision of Prof. Anna Vilanova (a.vilanova@tue.nl), dr. Sarah Schölten, and dr. Ana Teixeira Martins (van Gogh Museum).

PhD in Adaptive Connectivity Architectures for Cyber-Physical Systems

Introduction
Are you passionate about high-tech systems and in particular connectivity within and between complex system? Would you like to apply efficient AI techniques for adaptive configuration and optimization of industrial communication networks to enhance their reliability and real-time performance? Then apply for the PhD position on Adaptive Connectivity Architectures for Cyber-Physical Systems!

Job Description
Modern cyber-physical systems (CPS), such as semiconductor manufacturing machines and advanced mechatronic systems, rely on ultra-reliable, low-latency, and deterministic communication between distributed sensors, controllers, and actuators. Ensuring such performance under dynamic workloads, strict real-time constraints, and limited computational resources remains a key technical challenge. This PhD project addresses these challenges by developing efficient optimization methods for run-time network configuration and control. You will design efficient and lightweight learning-based techniques for automated scheduling, network resource allocation, and parameter setting, enabling fast and predictable adaptation with minimal overhead. A central focus is the co-design of algorithms with edge hardware and embedded platforms. You will investigate implementation strategies that account for limited compute, timing constraints, and energy efficiency, bridging theory and deployable solutions. This includes integration of wired and wireless communication technologies and support for run-time adaptation and fault resilience. The developed concepts will be validated through prototyping and experimental evaluation in realistic setups or digital twins, with performance assessed in terms of latency, reliability, and scalability, in close collaboration with academic and industrial partners.

Research group and lab:
The position will be hosted by the Networked Embedded Systems (NES-Lab) research Lab in the Electronic Systems (ES) group of the Electrical Engineering department. The ES group is a top research group and is world-renowned for its design automation and embedded systems research. Our ambition is to provide a scientific basis for design trajectories of electronic systems, ranging from digital circuits to cyber-physical systems. The trajectories are constructive and lead to high-quality, cost-effective systems with predictable properties (functionality, timing, reliability, power dissipation, and cost). Design trajectories for applications that have strict real-time requirements and stringent power constraints are an explicit focus point of the group. NES-Lab focusses on design, analysis, and optimization of reliable and low-power embedded networks as a main building block of the Internet-of-Things (IoT).

Job Requirements

• A master’s degree in Electrical Engineering, Computer Science, or a related discipline.
• A research-oriented attitude.
• Solid background in machine learning and optimization methods.
• Knowledge and experience in (wireless) communication and networking technologies, embedded system design, and computer programming (C++, Python, etc.).
• 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.

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.
• 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. Majid Nabi, m.nabi@tue.nl, head of the NES lab.

Visit our website for more information about the application process. You can also contact HRServices.ee@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.
• Copies of diplomas and transcripts (with course grades), proof of English proficiency,
• MSc thesis or any other relevant document in English of which you are the main author.

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.

Postdoc In Meta-science

Introduction
Are you fascinated by how science can become more transparent, reliable, and impactful? Do you want to contribute to improving research practices at the institutional level? Are you interested in understanding how policies shape researchers’ behavior—and how they can be redesigned to better support Open Science? If so, we invite applications for a postdoctoral position in a collaborative meta-science project on the effectiveness of data and code sharing policies in research-performing organizations.

Job Description
Open Science is an umbrella term encompassing, among other elements, the sharing of research data and code to facilitate verification and further development of research. It is widely endorsed by universities, funders, and policy makers, yet its implementation in daily research practice remains uneven. While many institutions have adopted policies encouraging or requiring researchers to share their data and code, the actual uptake of these practices varies considerably across organizations, disciplines, and research communities. This raises important questions: Which policy features promote meaningful behavioral change? Why do some institutional policies remain largely symbolic, while others lead to measurable improvements in transparency? And how can institutions design policies that are both effective and sensitive to researchers’ needs and constraints?

You will be part of a project that addresses these questions by combining policy analysis, behavioral research, and stakeholder co-creation. You will map existing institutional policies, examine researchers’ attitudes and actual data and code sharing practices, and identify which policy characteristics are associated with greater adoption. Building on these insights, you will collaborate with researchers, data stewards, and policy makers to develop improved, evidence-based policy frameworks that institutions can implement.

You will join an interdisciplinary team of meta-scientists and Open Science researchers and will play a central role in all stages of the project—from conceptual development and data collection to analysis, stakeholder engagement, and dissemination. This position offers a unique opportunity to contribute to research with direct policy relevance and to shape the future of Open Science practices at the institutional level.

Job Requirements

• Motivated researcher, with a PhD in meta-science, science of science, science and technology studies (STS), psychology, sociology, research methodology/statistics, philosophy of science, information science, or a related discipline.
• Methodological expertise in at least one—and preferably several—of the following: policy analysis, survey research, quantitative analysis, scientometrics, stakeholder co-creation.
• Advanced proficiency in R and python for statistical and corpus analysis
• Ability to conduct high quality academic research, reflected in demonstratable outputs.
• A team player who enjoys coaching Master's students and working in a dynamic, interdisciplinary team.
• A proven ability to manage complex projects to completion on schedule.
• Excellent (written and verbal) proficiency in English, good communication and leadership skills.

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 2 years.
• Salary in accordance with the Collective Labour Agreement for Dutch Universities, scale 10 (min. € 4,241 max. € 5,538).
• 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 Industrial Engineering & Innovation Sciences (IE&IS) department combines disciplinary knowledge from the humanities, social sciences and technical sciences to solve the complex problems of industries and society. We collaboratively focus on and create responsible and effective innovations for the research themes: Humans and Technology, Supply Chain Management, Sustainability and Circularity, and Value of Data-Driven Intelligence.

Information
Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager Krist Vaesen, Associate Professor, k.vaesen@tue.nl or +31 40 247 4987.

Visit our website for more information about the application process. You can also contact HRServices.ieis@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 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 Adaptive Connectivity Architectures for Cyber-Physical Systems

Modern cyber-physical systems (CPS), such as semiconductor manufacturing machines and advanced mechatronic systems, rely on ultra-reliable, low-latency, and deterministic communication between distributed sensors, controllers, and actuators. Ensuring such performance under dynamic workloads, strict real-time constraints, and limited computational resources remains a key technical challenge. This PhD project addresses these challenges by developing efficient optimization methods for run-time network configuration and control. You will design efficient and lightweight learning-based techniques for automated scheduling, network resource allocation, and parameter setting, enabling fast and predictable adaptation with minimal overhead. A central focus is the co-design of algorithms with edge hardware and embedded platforms. You will investigate implementation strategies that account for limited compute, timing constraints, and energy efficiency, bridging theory and deployable solutions. This includes integration of wired and wireless communication technologies and support for run-time adaptation and fault resilience. The developed concepts will be validated through prototyping and experimental evaluation in realistic setups or digital twins, with performance assessed in terms of latency, reliability, and scalability, in close collaboration with academic and industrial partners.

Research group and lab:
The position will be hosted by the Networked Embedded Systems (NES-Lab) research Lab in the Electronic Systems (ES) group of the Electrical Engineering department. The ES group is a top research group and is world-renowned for its design automation and embedded systems research. Our ambition is to provide a scientific basis for design trajectories of electronic systems, ranging from digital circuits to cyber-physical systems. The trajectories are constructive and lead to high-quality, cost-effective systems with predictable properties (functionality, timing, reliability, power dissipation, and cost). Design trajectories for applications that have strict real-time requirements and stringent power constraints are an explicit focus point of the group. NES-Lab focusses on design, analysis, and optimization of reliable and low-power embedded networks as a main building block of the Internet-of-Things (IoT).