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approximation algorithm, using linear programming methods. Over the next years, the project will grow to a collaborate team of 4-5 PhD students and Postdocs. The successful candidate will work directly with the
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of Artificial intelligence, Machine learning, Numerical simulation, Formal verification. Such methods include, among the others: AI-guided simulation of the mathematical models of the patho-physiology and PK/PD
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inverse problems. The team aims at developing Bayesian computational methods for such (ill-posed) inverse problems and aims both at increasing their validity and at reducing their computational cost. In
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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
materials science or a related discipline • Some knowledge of the theory of materials and experience with computational methods in materials science • Some experience with machine-learning interatomic
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or transformed from other maps. The ERC funded project GeoTrAnsQData project addresses this by developing a GeoQA method that converts questions into executable geo-analytical workflows, turning geodata into new
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interactions among neighbouring agents and their environments. The research will exploit an interdisciplinary approach that combines control theory, nonlinear dynamical systems, robotics, and formal methods
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from other maps. The ERC funded project GeoTrAnsQData project addresses this by developing a GeoQA method that converts questions into executable geo-analytical workflows, turning geodata into new answer
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world. We look forward to receiving your application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is
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candidate, you will develop methods that combine data-driven autonomy with formal safety guarantees and validate them in real time through simulation and experimental evaluation. The position is hosted
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, maps must be created or transformed from other maps. The ERC funded project GeoTrAnsQData project addresses this by developing a GeoQA method that converts questions into executable geo-analytical