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leader and follower decisions. We are seeking for a motivated and enthusiastic Postdoc (PD) researcher to join the Combinatorial Optimization group (https://www.tue.nl/en/research/research-groups
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streams, and PHA-X, a composite obtained from PHA-rich biomass. You will further develop, test and optimize the PHA-X material, you will, in close collaboration with two industrial partners, contribute
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on the results of the Erasmus+ project EDDIE. When it comes to the research part: This PostDoc project explores how artificial intelligence (AI) can be leveraged to optimize energy portfolios for local energy
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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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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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great level of autonomy. You stay in close contact with your project leader and team members to align your activities with those of others and ensure optimal progress; reporting the results of your work
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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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science); Making a limited contribution to teaching within the Department (up to a maximum of 10%) is possible; Adhering to the principles of open science and team science. Where to apply Website https