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- Delft University of Technology (TU Delft); Published yesterday
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- Delft University of Technology (TU Delft); Published 21 Nov ’25
- Delft University of Technology (TU Delft); Published today
- Delft University of Technology (TU Delft); yesterday published
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control (MPC) or reinforcement learning (RL) frameworks to compute optimal exoskeleton assistance in real time. Validating the developed methods in human experiments using motion capture, electromyography
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that strengthens collaboration, reduces time-to-job, and drives innovation for a climate-neutral society. In this position, you will design and optimize learning communities that integrate learning
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. Based on these insights, you will formulate design rules to predict optimal loading conditions and release mechanisms, supporting experimental optimization. We expect you to be able to work with a high
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into model-predictive control (MPC) or reinforcement learning (RL) frameworks to compute optimal exoskeleton assistance in real time. Validating the developed methods in human experiments using motion capture
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of the urban soil/subsoil in Amsterdam required for optimal tree growth. Ecosystem services include: storing water in the unsaturated zone; draining water via groundwater; sequestering carbon; filtering and
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of life and also increasing informal caregivers’ mental and physical burden. In the context of the larger project DREAm, this postdoc position will look at indoor environmental aspects and try to optimize
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. Background: You hold a PhD in Computer Science, Machine Learning, Electrical Engineering, Embedded Systems or related fields. Core Expertise: Strong expertise in Federated Learning and/or Continual Learning
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skills for functional genomic screen design and analysis. You will build CRISPR tools, design optimized pooled genetic screens (e.g. Perturb-seq–based approaches), and troubleshoot the experimental
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national and international researchers in the relevant field; Supervising research assistants and, where applicable, PhD candidates; Participating in meetings of the project research group and departmental
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and, where applicable, contributing to the supervision of PhD candidates; Being part of a collaborative research environment that promotes team science in the Stress, Pain, and Anxiety ResearCh (SPARC