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– from the modeling of material behavior to the development of the material to the finished component. PhD position on physics-based machine learning modeling for materials and process design Reference
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, candidates are required to complete a scientific programming task in the subject area of the advertised position: https://www.hpc.uni-wuppertal.de/de/peter-zaspel/challenge-in-bayesian-inference-for-climate
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into consideration. Please submit your complete application by sending it to secai-office@tu-dresden.de , preferably via the TU Dresden SecureMail Portal https://securemail.tu-dresden.de . Application Deadline
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improve the sustainability, the energy efficiency and the safety of industrial processes. The Department of Fluid Dynamics Resource Technology Processes is looking for a PhD Student (f/m/d) Experimental
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Research Areas: mechanical, fluid transport, photonic, and energy materials, supported by four Cross Areas dedicated to imaging, modeling, data, and exploitation. Together, they form a vibrant
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, candidates are required to complete a scientific programming task in the subject area of the advertised position: https://www.hpc.uni-wuppertal.de/de/peter-zaspel/challenge-in-bayesian-inference-for-climate
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of industrial processes. In a joint effort of both institutes, the Department AI4Quantum – Machine Learning for Quantum Simulation and Computing and Thermal Energy and Process Engineering are looking for a PhD
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focus on modelling radiation-induced chemical processes in biologically relevant media. Using a computational multiscale approach—including reactive molecular dynamics, irradiation-driven molecular
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. Knowledge of geoinformation technologies, 3D building modelling (BIM), and AI applications Knowledge of R and Python—especially spatial data science techniques Analytical and process-oriented thinking as
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. Knowledge of geoinformation technologies, 3D building modelling (BIM), and AI applications Knowledge of R and Python—especially spatial data science techniques Analytical and process-oriented thinking as