Jobid=618559931278759385 (0.0198)
ppbDevelop scientific machine learning and control methods for biofabrication and contribute to next generation biomedical and sustainable technologies /b /p h3Job description /h3 pAre 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. /p pThe 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. /p pAs 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. /p pThis 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. /p h3Job requirements /h3 pYou have: /p ul liA PhD degree in applied mathematics, computational science, machine learning, or a closely related field /li liStrong expertise in numerical and scientific computing, including scientific machine learning /li liStrong programming skills (preferably Python or Julia) and the ability to work with additional scientific computing tools /li liA background in, or strong interest in, biomechanics and the modeling of soft biological tissues /li /ul pThe following are considered advantages: /p ul liExperience with operator learning methods (e.g., neural operators, DeepONet, Fourier neural operators) /li liExperience with inverse problems, optimization, and control of PDE-based systems /li liExperience with image data processing and modeling /li liExperience in interdisciplinary collaborations /li /ul h3TU Delft as an employer /h3 pTU Delft is a top international university combining science, engineering and design. It addresses challenges in areas including energy, climate, mobility, health and digital society, offering a strong international research environment. /p h3Faculty of Electrical Engineering, Mathematics and Computer Science /h3 pThe Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines that reinforce each other. The faculty contributes to areas such as sustainable electricity systems, future chips and sensors, AI, and applied mathematics research including disease process mapping using single cell data and simulation of large-scale natural phenomena. There is ample room for ground-breaking research, supported by excellent labs and facilities in a strong international environment. /p h3Conditions of employment /h3 ul liWe offer a temporary contract for 12 months, with the possibility of extension up to a maximum of 28 months. /li liSalary and benefits in accordance with the Collective Labour Agreement for Dutch Universities. /li liAn excellent pension scheme via the ABP. /li liThe possibility to compile an individual employment package every year. /li liDiscount with health insurers on supplemental packages. /li liFlexible working week. /li liEvery year, 232 leave hours (at 38 hours). You can also sell or buy additional leave hours via the individual choice budget. /li liPlenty of opportunities for education, training and courses. /li liPartially paid parental leave. /li liAttention for working healthy and energetically with the vitality program. /li liRelocation support for moving to the Netherlands, including services and events to help you settle in Delft. /li liA Dual Career Programme to support an accompanying partner with their job search in the Netherlands. /li /ul /p #J-18808-Ljbffr
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