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research methods. The overarching goal of LAFI is to understand and quantify L-A feedbacks via unique synergistic observations and model simulations from the micro-gamma (» 2 m) to the meso-gamma (» 2 km
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Research or a related field Strong knowledge of quantitative and/or computational research methods, ideally in numerical optimization and simulation models. Proficiency in one of the major programming
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the interface of education and social work and ambition to advance one of the aforementioned subject areas Training and experience in qualitative empirical methods/mixed methods research Openness to further
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with regard to numerical simulation of the impact of detonation and deflagration events on infrastructure and modelling of structural behaviour under highly dynamic loads Provision of scientific and
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mechanics, applied mathematics, biomedical engineering, computer science or a closely related discipline Strong background in finite-element methods, continuum mechanics and numerical analysis Excellent
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to work on the discovery of new superconducting materials with high critical temperatures, using novel methods and concepts such as machine learning and quantum geometry. The project is related to large
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an innovative multi-method design, the project integrates: Daily diary and ecological momentary assessment (EMA) approaches In-depth qualitative and immersive fieldwork conducted by the geography team A key
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of the methods. The project is carried out in close collaboration with Helical-AI, an industrial partner specialized in large-scale genomic foundation models and HPC-enabled model deployment, ensuring
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problems that arise with analyses or other aspects of research projects; Contributing to reports, presentations and publications by preparing numerical and graphical summaries (visualizations) using relevant
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, computational mechanics, computer science, applied mathematics or similar Strong experience with deep learning, e.g. PyTorch, JAX, TensorFlow, and probabilistic methods Familiarity with graph neural networks