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Field
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problem. Do a theoretical analysis of the assigned problem using tools from information theory and related fields. Design a solution which is implementable on a computer and in hardware. Collaborate with
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fields. Design a solution which is implementable on a computer and in hardware. Collaborate with colleagues to implement the solution in hardware at the ACES lab at the Institute of Theoretical
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data • Design clinically meaningful benchmarks and robust evaluations • Publish at leading machine learning and medical AI venues • Collaborate with clinicians, computer scientists, and European partners
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CFD simulations, led to a shift from performance towards efficiency, defined more precisely as minimisation of computer resources required for a given simulation. Concomitantly, the objective
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12.01.2026, Academic staff The Professorship of Machine Learning at the Department of Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13 100%; initial contract 1.5
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Neurosciences Computer Graphics and Ecological Informatics Computer Networks Computer Security Databases and Information Systems Data Fusion Data Science Infrastructures Didactics of Informatics Digital
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professor from the natural sciences and a professor from information/computer/mathematical science. The DASHH graduate curriculum comprises 18 ECTS and is divided into the following branches: knowledge gain
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Top-ranked Master's degree in robotics, computer vision, system control, machine learning, mathematics, or a related field (background in any of the following); Being excited to make a real impact with
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physics or theoretical chemistry, with interest in electronic-structure theory and method development. Experience in computer simulations and programming is advantageous. Very good communication and writing
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services at approx. 3,650 EUR per year for prospective students. The package covers accommodation, meals, health insurance, public transport, a personal computer and Internet access. Funding opportunities