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for light trapping in thin-film solar cells .” You will become part of an enthusiastic team working closely with collaborators at DTU Physics and DTU Nanolab to advance neural network-based methods
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learning architectures including generative models, particularly for sequence or structural data (e.g. transformers, graph neural networks) Proved experience in working independently and as part of a
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-learn, PyTorch) or physics-informed neural networks for thermal systems is a plus. Excellent communication and collaboration skills across disciplines. We offer DTU is a leading technical university
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of complex microfluidic hydrogel networks, integration of micropumps for bubble-free aseptic perfusion, and non-contact mapping of multiple metabolites during tissue culture. You will be working on all aspects