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of novel physics-guided AI algorithms for drug design, integrating physics-based modeling with state-of-the-art deep learning methods. The project will focus on creating a next-generation docking framework
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models to the design of sustainable processes in chemical engineering. To bridge the scale from molecules to processes, we apply state-of-the art mathematical concepts and tools combined with highly
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carbohydrates, namely the core-1-derived O-glycans, shape the lymphatic endothelial cell glycocalyx and its recognition by myeloid immune cells. Job description The successful candidate will: Learn to work with
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cells Key methods will include: Gaussian Processes (heteroscedastic & multivariate) Operator-valued and deep kernels Active Bayesian experimental design Physics-informed neural networks Closed-loop
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Bern and Zurich to develop multi-compartments OOC systems integrating blood and lymphatic vasculature with key mechanical cues of the lung microenvironment Characterization: Design chips based
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affects plant–herbivore interactions by measuring color impacts on leaf chemistry, herbivore-specific damage patterns and herbivore behavior. You will be responsible for growing your experimental plants and
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experience in at least some of the following areas: (magneto-)optical spectroscopy at cryogenic conditions, exfoliation and stacking of two-dimensional materials, design and handling charge-tunable
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to design and execute a pilot CO₂ injection into a saline aquifer to test its behaviour and migration. This PhD project forms part of this study that aims to assess the feasibility, safety, and societal
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solution for through-thickness reinforcement of FRPs. Your tasks You will work in collaboration with a postdoctoral researcher/scientist mainly on: Design and manufacturing of SMA Z-pinned FRPs SMA
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of two-dimensional materials, design and handling charge-tunable semiconductor structures, exfoliation and stacking of two-dimensional materials and fabrication of vdW heterostructures, clean-room