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modules What you contribute Master's student in computer science or an equivalent subject at a Berlin university or college with above-average grades Very good programming skills in Python and proficiency
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Professor (W3 / W2 with Tenure Track to W3) for Materials and/or devices for Photonics and Quantum T
devices for photonic and quantum optical applications. The primary focus of the research will be on material synthesis, processing, and characterization, and/or the development of devices for photonics and
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experimental projects. As part of these projects, you will manage primary lung cell cultures (including patient-based material), work with lung organoids and organoid-immune cell co-culture systems, perform
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characterization of primary cells, FACS and histological techniques Fluorescence microscopy, Ca2+ imaging, or electrophysiology Molecular biology, biochemical techniques, and gene expression analysis Animal
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collaborations and spin-offs. With around 1,300 employees, we conduct research in four main areas: energy supply, energy distribution, energy storage and energy use. The state-of-the-art R&D infrastructure
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processes. Excellent peer-reviewed publication record in high-impact journals on topics related to the modelling of ocean biogeochemical processes such as nitrogen fixation, primary production, or nutrient
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atmosphere in an international team Modern and excellently equipped workspace in central location Opportunities to write a master's or bachelor´s thesis Flexible working hours The position is initially limited
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real-time sensor technology using deflectometric methods. After successful introduction, there is the opportunity to work on the topic as part of a thesis (bachelor's or master's thesis). Be part of
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from limited path-following accuracy, high parameterization effort, and inappropriate behavior on slippery surfaces. The goal of the master's thesis is to develop a model predictive path follower
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parameterization and shows limited adaptability to different vehicle types and operating conditions. The aim of the master's thesis is to investigate an end-to-end reinforcement learning approach for path tracking