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Field
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mechanistic process models with machine learning for accuracy, generalization, and interpretability. Uncertainty-aware AI: robust inference under noise, drift, and changing conditions; knowing when a model is
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methods to accelerate the discovery of better catalysts that use less platinum and have improved long-term stability. By combining large-scale density functional theory with machine-learned interatomic
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acquisition to machine learning interpretation to 3D geometry and quality models to machine learning decision support. The PhD project will be carried out in close cooperation with Norwegian tunnelling
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sciences, law, and philosophy. Four WPs address citizen-empowerment-scenarios (CES) in healthcare, mobility, public governance, and healthy living. Each PhD position is embedded in one work package and
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. Strong (inter-)national network in field of application. Experience with high-performance computing (HPC) and large datasets. Experience with machine learning applied to geophysical signals. Experience in
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. Umeå University is offering a PhD position in Computing Science with a focus on machine learning for graph
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and CH4) from headwaters, and use of machine learning and process-based model for large scale assessments and projections of the land-water carbon cycle to variation in climate conditions. The detailed
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the Arctic, experimental tests of climate driven changes in carbon export from land and turnover and release of greenhouse gases (CO2 and CH4 ) from headwaters, and use of machine learning and process-based
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RPTU University Kaiserslautern-Landau • | Kaiserslautern, Rheinland Pfalz | Germany | about 11 hours ago
communication tailored to their needs. For further information, see: https://www.physik.uni-kl.de/oscar/ Course organisation During the research work, the PhD student has the possibility to participate in
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establish a digital route to quantify the segregation behaviour of residual elements at austenite/austenite grain boundaries through atomic-scale simulations, using modern machine learning techniques and in