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reinforcement learning methods can be used to solve multiobjective discrete and combinatorial optimization problems. The goal is to develop new algorithmic approaches that combine ideas from machine learning
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, including droughts, fires, and deforestation, as well as ecosystem resilience and tipping points. Key Responsibilities The successful PhD candidate will: Develop and apply machine learning methods (e.g. CNNs
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08.09.2021, Academic staff The Professorship of Machine Learning at the Department of Electrical and Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13 100%, 3
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07.04.2026, Academic staff PhD position at the interface of computational physics, machine learning, and experimental reactor design. The project focuses on developing PINN-based simulations
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with a focus in economics, or related disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high
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processing parameters. You will develop machine learning models to analyse experimental datasets and uncover structure-function relationships that determine membrane performance. By combining statistical
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Research Responsibilities Conduct original PhD research on shared and remote robotic control under uncertain communication conditions, integrating passivity-based control, machine learning, and human
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Optimization (DPO) and reinforcement learning from human feedback, building preference datasets together with clinicians - Build and run a Red Team process with physicians, computer scientists, and patient
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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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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