238 embedded-system "https:" "https:" "https:" "https:" "Cardiff University" positions at Technical University of Munich in Germany
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is embedded in an internationally visible research group with strong ties to leading academic institutions and industry partners, providing excellent opportunities for interdisciplinary collaboration
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• Position: TV-L E13 (65%), with the opportunity to pursue a doctoral degree (PhD) • Start date: from July 2026 • Research community: The position is embedded in the BioSysteM Cluster of Excellence, offering
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using the model iLand. The work is embedded in the BETA-FOR project (https://www.uni-wuerzburg.de/for5375/) and will collaborate closely with the Forest Economics and Sustainable Land-use Planning group
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02.04.2026, Academic staff The research group of Prof. Frank Ortmann at TUM is offering two PhD positions (Research Associates, f/m/d) Start: Summer 2026 (flexible) Research topics: * Optical
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13.01.2026, Wissenschaftliches Personal The Professorship of Fungal Biotechnology in Wood Science at TUM has an open position for a doctoral researcher (TV-L contract 3.75 years) on Systems biology
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applications for a Postdoctoral Researcher position. The position is embedded in the TUM SEED Centre and the TUM ENERGISE Centre, international research networks connecting leading universities across Europe
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) to work on the development of an Amazonian Early Warning System (AmEWS) integrating Earth Observation data, process-based ecosystem models, and advanced machine learning approaches. The position is embedded
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in cancers of unknown primary (CUP). Your Role You will join Subproject 3 (Model Alignment and Optimization), led by PD Dr. Keno Bressem (https://scholar.google.com/citations?user=wIEgwbkAAAAJ&hl=en
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environment? Then let’s design the energy management systems of the future together! Our research focus: The researchers working at the Professorship of Energy Management Technologies are focusing on the design
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grounded and practically viable reinforcement learning frameworks for spacecraft systems, where safety is guaranteed at all times through shielding, while still allowing learning-based performance