PhD Position POLARIS Materials

Job description

Thermal management in highly integrated systems like high-performance computing and communication arrays is a large challenge in the electronics industry. When temperature of devices increases their lifetime reduces and thermal trottling can reduce performance. In highly integrated systems, the volume of material to trasnport heat is reduced which requires innovations for effective heat management. Improving the materials used is one of the methods that can be employed for this. Especially nanomaterials are of interest here, as they can potentially outperform bulk materials.

In this project at the faculty of EEMCS, which is part of the growth fund POLARIS, you will investigate innovate nanomaterial solutions for thermal management. The application of the material can be as thermal interface material or by enlabling other heat dissipation pathways. As part of the research, you will synthesize the materials in the EKL cleanroom, create test structures and validate their thermal performance and other relevant materials properties.

The research will take place in the Electronic Components, Technology and Materials section of the Microelectronics Department. Within the consortium, also other academic, research and industrial partners are present with whom there can be opportunities to collaborate.

Job requirements
You hold a MSc in electrical engineering, materials science, applied physics or other relevant fields. Prioir experience in cleanroom processing and/or thermal or material analysis is highly recommended.

TU Delft (Delft University of Technology)
Working at TU Delft means contributing to solutions that really make a difference.

For over 180 years, we have been training engineers who make an impact worldwide in companies, government bodies, or as entrepreneurs. Our alumni turn knowledge into concrete solutions for the challenges of today and tomorrow. These challenges are changing rapidly. That is why we focus on themes such as energy, climate, digitalisation, artificial intelligence (AI), and smart mobility every day. Our education and research are directly aligned with what society needs now and in the future.

At TU Delft, our people make the difference. With their knowledge and curiosity, our staff provide a high-quality education and conduct pioneering research that extends beyond the campus. You will have the opportunity to take the initiative, work with others, and grow as a professional. Working at TU Delft means join an international community of professionals and students. Together, we create knowledge, innovations, and solutions that help move the world forward.

Faculty of Electrical Engineering, Mathematics and Computer Science
The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines. Combined, they reinforce each other and are the driving force behind the technology we all use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we are pushing the limits of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: there is plenty of room at the faculty for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1000 employees and 4,000 students work and study in this innovative environment.

Click here to go to the website of the Faculty of Electrical Engineering, Mathematics and Computer Science.

PhD Position to Help Shape the Future of Reliable, Sustainable Power Electronics

Job description

The successful candidate will conduct research within the EU-funded Moore4Power project on health monitoring and predictive reliability of advanced power electronic modules and systems. Location will be in Delft, as a team member of ECTM, in close collabrations with NL and EU industrial partners.

Job requirements

• MSc degree in Electrical & Mechanical Engineering, Power Electronics, Materials, or a closely related discipline.
• Strong background in power electronics, electronic paakging and system integration, reliability, sensors and instrumentation.
• Knowledge of one or more of the following areas:Power electronics, Reliability and failure mechanisms, Sensors and instrumentation, Data analytics, machine learning, or AI, Digital twins or physics-based modeling, wide-bandgap semiconductor technologies.
• Excellent written and spoken English communication skills.
• Ability to work independently while contributing effectively to collaborative research teams.

TU Delft (Delft University of Technology)
Working at TU Delft means contributing to solutions that really make a difference.

For over 180 years, we have been training engineers who make an impact worldwide in companies, government bodies, or as entrepreneurs. Our alumni turn knowledge into concrete solutions for the challenges of today and tomorrow. These challenges are changing rapidly. That is why we focus on themes such as energy, climate, digitalisation, artificial intelligence (AI), and smart mobility every day. Our education and research are directly aligned with what society needs now and in the future.

At TU Delft, our people make the difference. With their knowledge and curiosity, our staff provide a high-quality education and conduct pioneering research that extends beyond the campus. You will have the opportunity to take the initiative, work with others, and grow as a professional. Working at TU Delft means join an international community of professionals and students. Together, we create knowledge, innovations, and solutions that help move the world forward.

Faculty of Electrical Engineering, Mathematics and Computer Science
The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines. Combined, they reinforce each other and are the driving force behind the technology we all use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we are pushing the limits of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: there is plenty of room at the faculty for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1000 employees and 4,000 students work and study in this innovative environment.

Click here to go to the website of the Faculty of Electrical Engineering, Mathematics and Computer Science.

Postdoc for E2Packman

Job description

The successful candidate will conduct research within the EU-funded E2Packman project on advanced electronic packaging. Location will be in Delft, as a team member of ECTM, in close collabrations with NL and EU industrial partners.

Job requirements

• Strong research background in more of the following: electronic packaging, heterogeneous integration, thermal management, reliability, multi-physics and multiscale simulation.
• Demonstrated publication record in high-quality international journals and conferences.
• Experience working in multidisciplinary and international research environments.
• Excellent analytical, communication, and scientific writing skills.
• Ability to work independently while contributing effectively to collaborative research teams.
• Track record of supervising MSc and PhD students and participation in teaching activities.
• Good command of spoken and written English.

TU Delft (Delft University of Technology)
Working at TU Delft means contributing to solutions that really make a difference.

For over 180 years, we have been training engineers who make an impact worldwide in companies, government bodies, or as entrepreneurs. Our alumni turn knowledge into concrete solutions for the challenges of today and tomorrow. These challenges are changing rapidly. That is why we focus on themes such as energy, climate, digitalisation, artificial intelligence (AI), and smart mobility every day. Our education and research are directly aligned with what society needs now and in the future.

At TU Delft, our people make the difference. With their knowledge and curiosity, our staff provide a high-quality education and conduct pioneering research that extends beyond the campus. You will have the opportunity to take the initiative, work with others, and grow as a professional. Working at TU Delft means join an international community of professionals and students. Together, we create knowledge, innovations, and solutions that help move the world forward.

Faculty of Electrical Engineering, Mathematics and Computer Science
The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines. Combined, they reinforce each other and are the driving force behind the technology we all use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we are pushing the limits of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: there is plenty of room at the faculty for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1000 employees and 4,000 students work and study in this innovative environment.

Click here to go to the website of the Faculty of Electrical Engineering, Mathematics and Computer Science.

Riooloperator Riooldienst

Als operator Riooldienst zorg je ervoor dat het Rotterdamse rioolstelsel goed blijft functioneren. Je reinigt hoofdriolen, pompputten, kolken en lijngoten en grijpt in bij storingen, verstoppingen en calamiteiten. Daarmee lever je een directe bijdrage aan een veilige, schone en goed bereikbare stad.

Je werkt met specialistisch materieel, zoals kolkenzuigers, hogedrukwagens en hoofdrioolzuigers. Je weet hoe je deze voertuigen veilig en efficiënt inzet en voert waar nodig klein preventief onderhoud uit. Geen dag is hetzelfde: de ene keer reinig je een hoofdriolering, de andere keer reinig je kolken en lijngoten of los je een acute verstopping op of ga je op pad naar aanleiding van een melding uit de stad.

Samen met je collega's vorm je een hecht team dat op elkaar kan rekenen. Je draait mee in de 24/7 beschikbaarheidsdienst en bent ook inzetbaar tijdens de winterdienst. Juist wanneer de stad op je rekent, sta jij klaar.
Je belangrijkste werkzaamheden:

• Reinigen van hoofdriolen, pompputten, kolken en lijngoten;
• Verhelpen van meldingen en storingen via het Meldsysteem Buitenruimte (Fixi/FIXR);
• Oplossen of veiligstellen van gebreken aan onderdelen van het rioolstelsel;
• Werken met specialistische voertuigen, hogedrukapparatuur en vacuümsystemen;
• Uitvoeren van klein preventief onderhoud aan voertuigen en materieel;
• Zorgdragen voor een veilige uitvoering van de werkzaamheden;
• Deelnemen aan beschikbaarheidsdiensten en de winterdienst.

2 PhD positions in Physics‑Informed Machine Learning for Traffic Modelling & Prediction

Job description

Road traffic is a highly complex dynamic system. Minor disruptions can lead to major delays with traffic jams spreading like oil spills over entire networks. We believe traffic management based on reliable predictions is therefore crucial to ensure accessibility and safety, especially during major events, accidents and extreme weather. In a new project called deepTraffic (funded by the Dutch science foundation NWO), we aim to develop a new generation of traffic prediction methods, combining traffic flow theory with machine learning, and with that, the best of both worlds: theory and logic where necessary, data-driven where possible. This innovative new approach enables more efficient and robust management of large traffic networks under all conditions.

You have the most important role in this ambitious project as one of the young talents in our team. We have 2 PhD and one postdoc positions, all of whom will be supervised by a highly experienced team of four (top) researchers in this field supported by a technician. You will work in a highly collaborative team where your ideas matter from day one, indepenent thinking is encouraged, and you will get all the support you need to further develop your scientific career.

PhD Position 1 - Hybrid Traffic Flow Modelling

This PhD focuses on developing hybrid traffic flow models that combine physical modelling principles with machine learning approaches, such as Physics-Informed Neural Networks (PINNs) and machine-learning-enhanced traffic models.

You will:

• Develop next-generation hybrid traffic flow models that combine traffic theory with machine learning
• Investigate Physics-Informed Neural Networks (PINNs) and related approaches for network-wide traffic prediction
• Design physically consistent and interpretable machine-learning methods for dynamic traffic systems
• Test and validate prediction models using large-scale real-world traffic data from Dutch freeway networks.

PhD Position 2 - Data Assimilation and Network State Estimation
This PhD focuses on estimating key traffic states and inputs, such as path flows, boundary conditions, and other dynamic network variables.

You will:

• Develop new data assimilation methods for estimating traffic states and network conditions
• Combine machine learning with traffic flow theory to improve prediction reliability and robustness
• Estimate path flows, boundary conditions, and other key inputs for large-scale traffic models
• Design scalable methods for real-time traffic prediction and uncertainty quantification in operational networks.

The connection with practice is super important. This project is not just an academic exercise. We will work closely together with road authorities, traffic management centers, and industry to implement these prediction methods and test them against real constraints, with real data in real use cases on the Dutch freeway network. Herein, explainability and trustworthiness are key: traffic management using predictions may render those very same predictions invalid. Predictions need to come with confidence bounds and a narrative that make them usable in decision-support systems for operators and strategic advisors.

Job requirements
We look for highly motivated, collaborative and creative candidates. Do you recognize yourself in (many of) these requirements?

• You hold an Msc degree in a STEM field.
• You love physics and complex systems and are either familiar with, or very eager to learn about, (road) network traffic flow theory and simulation.
• You are a machine learning enthusiast (and realist).
• You love coding and have proven experience in e.g. Python, Matlab, JAVA, C#.
• You can present and communicate your ideas with AND without LLMs.
• You get excited about implementing your ideas.
• You are a team-player: you enjoy sharing ideas and solving puzzles together.
• You also enjoy digging in and solving puzzles independently.
• You believe in, and want to contribute to, an inclusive, open and safe workspace.

TU Delft (Delft University of Technology)
Working at TU Delft means contributing to solutions that really make a difference.

For over 180 years, we have been training engineers who make an impact worldwide in companies, government bodies, or as entrepreneurs. Our alumni turn knowledge into concrete solutions for the challenges of today and tomorrow. These challenges are changing rapidly. That is why we focus on themes such as energy, climate, digitalisation, artificial intelligence (AI), and smart mobility every day. Our education and research are directly aligned with what society needs now and in the future.

At TU Delft, our people make the difference. With their knowledge and curiosity, our staff provide a high-quality education and conduct pioneering research that extends beyond the campus. You will have the opportunity to take the initiative, work with others, and grow as a professional. Working at TU Delft means join an international community of professionals and students. Together, we create knowledge, innovations, and solutions that help move the world forward.

Faculty of Civil Engineering and Geosciences
The Faculty of Civil Engineering & Geosciences (CEG) is committed to outstanding international research and education in the field of civil engineering, applied earth sciences, traffic and transport, water technology, and delta technology. Our research feeds into our educational programmes and covers societal challenges such as climate change, energy transition, resource availability, urbanisation and clean water. Our research projects are conducted in close cooperation with a wide range of research institutions. CEG is convinced of the importance of open science and supports its scientists in integrating open science in their research practice. The Faculty of CEG comprises 28 research groups in the following seven departments: Materials Mechanics Management & Design, Engineering Structures, Geoscience and Engineering, Geoscience and Remote Sensing, Transport & Planning, Hydraulic Engineering and Water Management.

Click here to go to the website of the Faculty of Civil Engineering & Geosciences.