Develop scientific machine learning and control methods for biofabrication and contribute to next generation biomedical and sustainable technologies
Job description
Are you an ambitious researcher in applied mathematics or machine learning who wants to develop new methods, apply them in practice, and contribute to real-world impact? We invite applications for a postdoctoral position in the Numerical Analysis group at the Delft Institute of Applied Mathematics (DIAM), part of the Faculty of Electrical Engineering, Mathematics & Computer Science (EEMCS) at TU Delft. The position is embedded in the NWO Perspectief Project FAB4FUTURE, an interdisciplinary programme that develops next-generation biofabrication technologies for regenerative medicine and sustainable food production. A central goal is to create an artificial intelligence (AI)-driven toolbox for the bioprinting process, enabling accurate, efficient, and scalable control of complex biofabrication workflows.
The project brings together a broad consortium of academic partners, including Delft University of Technology, Maastricht University, UMC Utrecht, Utrecht University, and Zuyd University of Applied Sciences. It further includes industrial partners such as Axolotl, Demcon, Mosa Meat B.V., Poietis, RDInnovation, ReGEN Biomedical B.V., Scinus, and Xolo. Societal partners include Cellulaire Agricultuur Nederland, Dutch CardioVascular Alliance, Good Food Institute Europe, and Stichting AVS Proefdiervrij, ensuring strong links between fundamental research, technological development, and real-world application.
As a postdoctoral researcher, you will develop scientific machine learning methods for modeling and control of biofabrication processes, with a focus on cell and material deposition in soft, deformable biological systems. You will design approaches based on state-of-the-art techniques, such as convolutional neural networks, transformer models, operator learning, and optimization and control methods, while embedding morphoelastic and biomechanical models into the learning process. Your work will contribute directly to the AI-driven toolbox through predictive modeling, parameter optimization, and real-time control of the bioprinting process. You will apply and validate these methods on experimental data in close collaboration with leading biofabrication groups at UMC Utrecht and Maastricht University. This includes the development of surrogate models, inverse modeling, and integrated learning and control strategies. You will leverage experimental data from these partners while contributing insights to refine and improve biofabrication hardware and protocols.
This position offers a unique opportunity to advance scientific machine learning for control while contributing to impactful technologies in healthcare and sustainability. You will work in a highly interdisciplinary environment and are encouraged to actively shape the research direction, publish in leading venues, and build collaborations across disciplines.
Job requirements
You have:
- A PhD degree in applied mathematics, computational science, machine learning, or a closely related field
- Strong expertise in numerical and scientific computing, including scientific machine learning
- Strong programming skills (preferably Python or Julia) and the ability to work with additional scientific computing tools
- A background in, or strong interest in, biomechanics and the modeling of soft biological tissues
The following are considered advantages:
- Experience with operator learning methods (e.g., neural operators, DeepONet, Fourier neural operators)
- Experience with inverse problems, optimization, and control of PDE-based systems
- Experience with image data processing and modeling
- Experience in interdisciplinary collaborations
TU Delft (Delft University of Technology)
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.
At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.
Challenge. Change. Impact!
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.
, Mathematics and Computer Science.
Conditions of employment
- We offer a temporary contract for 12 months, with the possibility of extension up to a maximum of 28 months.
- A job of 32-38 hours per week.
- Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.
- An excellent pension scheme via the ABP.
- The possibility to compile an individual employment package every year.
- Discount with health insurers on supplemental packages.
- Flexible working week.
- Every year, 232 leave hours (at 38 hours). You can also sell or buy additional leave hours via the individual choice budget.
- Plenty of opportunities for education, training and courses.
- Partially paid parental leave
- Attention for working healthy and energetically with the vitality program.
Will you need to relocate to the Netherlands for this job? TU Delft is committed to make your move as smooth as possible! The HR unit, Coming to Delft Service, offers information on their website to help you prepare your relocation. In addition, Coming to Delft Service organises events to help you settle in the Netherlands, and expand your (social) network in Delft. A Dual Career Programme is available, to support your accompanying partner with their job search in the Netherlands. .
Additional information
If you would like more information about this vacancy or the selection procedure, please contact Alexander Heinlen, via [email protected].
Application procedure
Are you interested in this vacancy? Please apply no later than 9 June 2026 upload the following documents:
- A cover letter of at most one page describing your motivation and qualifications
- A curriculum vitae, including a list of publications
- Copies of degree certificates and transcripts
- A copy of your PhD thesis, or a draft version if applicable
You can address your application to Alexander Heinlen.
Please note:
- You can apply online. We will not process applications sent by email and/or post.
- As part of knowledge security, TU Delft conducts a risk assessment during the recruitment of personnel. We do this, among other things, to prevent the unwanted transfer of sensitive knowledge and technology. The assessment is based on information provided by the candidates themselves, such as their motivation letter and CV, and takes place at the final stages of the selection process. When the outcome of the assessment is negative, the candidate will be informed. The processing of personal data in the context of the risk assessment is carried out on the legal basis of the GDPR: performing a public task in the public interest. You can find more information about this assessment on our website about knowledge security.
- Please do not contact us for unsolicited services.
