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relevant component development tasks in the project Contribute to relevant simulation and modelling activities in the project Required selection criteria You must have a professionally relevant background in
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, training deep learning models to adapt designs to boundary conditions, and integrating FEM workflows within parametric modeling environments like Grasshopper. The candidate will contribute to building a
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affecting the intestinal epithelium and the possible partners related to the different phases of the disease. The work will be a combination of ex vivo organoid cultures, in vivo disease models, combined with
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, and entrepreneurship. Doctoral Candidates will gain transferable skills and learn from industry role models, equipping them to make significant contributions to solving the AMR crisis. The succsesssful
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leader will be the Head of Department. About the project Modern control systems rely on being at least partially predictive while digital twins also must maintain a state model of the targeted cyber
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other are developing regulations that provides both incentives and constraints for the energy transition and emission reduction. The research objective of the PhD is to develop models that captures
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, compared with state-of-the-art rule-based methods as baselines. Design of control barrier functions (CBFs) considered for safeguarding control setpoints. Dynamic programming and model-predictive control
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participate in the planning and running of experiments and be in charge of analyzing data from experiments, as well as performing modeling work of the phenomenon. The candidate will collaborate closely with