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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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adjustment and plate tectonics. The analysis will encompass both network wide and local analysis using data from Greenland GNSS Network (GNET). You will target specific regions where there can be an unresolved
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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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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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contribute to other educational activities Attract research funding with support from the department Disseminate your work at conferences and peer-reviewed journals Network with international research bodies
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of microbial biogeochemistry Proficiency in scientific programming and data analysis using tools such as Python, R, MATLAB, or similar Excellent written and verbal communication skills in English