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behavior, resolver interactions, and dependency modeling. This project builds on ongoing collaborations with both national and international partners, including SIDN and TNO in the Netherlands, CAIDA in
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inductive biases, we aim to identify key mechanisms that drive rapid learning in the visual system. The goal is to create a robust mechanistic neural network model of the visual system that not only mimics
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comprehensive and trustworthy AI models for subtype prediction, significantly influencing clinical decisions and personalized treatment strategies. The current project funded by NWO is a continuation of the
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collaborate closely with GGS partners across academia, industry, and government. Key responsibilities Developing life cycle inventories (LCA PhD) or material flow models (MFA PhD) in close collaboration with
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collaborate closely with GGS partners across academia, industry, and government. Key responsibilities Developing life cycle inventories (LCA PhD) or material flow models (MFA PhD) in close collaboration with
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science, computer science, applied mathematics or a related field a strong background in machine learning, material modeling, and metals processing, modeling and simulation (This will be a clear advantage
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to regenerative agriculture. The aim is to make regenerative agriculture the new normal by 2040. We are looking for a PhD candidate who can develop mathematical models that help understand which soil
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, the PhD researcher will develop physiological-model-based artificial intelligence technologies to assess patients’ recovery process, detect or even predict the occurrence of clinical adverse events like
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in thermodynamics, optimization, and control theory. Strong understanding of mathematical modeling, numerical optimization, and/or model predictive control (MPC). Experience working with large-scale
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powerful, modern analytical techniques including chemical proteomics and metabolomics. They will have access to advanced synthesis facilities, as well as biological models, such as macrophages and organoids