12 model-predictive-control Postdoctoral positions at Technical University of Denmark
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stability prediction Collaborating closely with experimental researchers for iterative model refinement Publishing high-impact research at the intersection of AI and molecular design Supervising students
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to simulate full system performance. You will work closely with the project partners to determine design specifications and also after the prototype has been realized to compare model prediction with actual
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considered an advantage if you have: Experience with protein language models (e.g., ESM, ProtT5) Experience with structure prediction frameworks Experience in geometric deep learning or graph neural networks
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have extensive knowledge on processes governing cross-shore transport and can use experimental data to develop predictive models. Experiences within numerical modelling of coastal processes is considered
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on Nanoparticles You will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include: Advancing equivariant neural network
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materials, (d) Artificial Intelligence (AI) models to predict and control the construction process, (e) a digital twin / information backbone that enables cohesive operation of the design and production
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, aimed at uncovering the key traits that define successful microbial biofertilizers, and to develop predictive models that can guide the rational design of next-generation BioAg products tailored
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Postdoctoral Positions in PFAS Analytics, Degradation, and Thermophysical Properties - DTU Chemistry
thermophysical properties vary across the diverse PFAS chemical space and how these properties may be predicted using computational models. These positions offer an excellent opportunity for early‑career
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pMHC multimers Functional evaluation of T cells General knowledge related to cancer immunology and antigens Experience with T cell epitope mapping and ligand predictions Immunology: T cell recognition