213 post-doc-in-wireless-communication-and-networks-2016 Postdoctoral positions in Germany
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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
Area of research: Scientific / postdoctoral posts Starting date: 01.07.2025 Job description: Postdoc (f/m/d): Machine Learning for Materials Modeling With cutting-edge research in the fields
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experts from companies and policy makers, as well as communicating findings in policy-relevant time frames and formats. This position is suitable for furthering scientific training according to Section 2
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communication skills What we can offer you: an inspiring and collaborative environment where you can fully realize your passion for education and research, autonomy supportive research environment and diverse
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independently and collaboratively in an interdisciplinary team Strong communication and organizational skills Our offer The position offers a dynamic, international, and interdisciplinary research environment
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using DC and AC-based techniques such as characteristic curves, cyclic voltammetry and electrochemical impedance spectroscopy Participation in the investigation of stacks and cells post-test using
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(including the doctoral dissertation) Strong methodological training in quantitative survey and experimental research (Additional asset: experience with using large language models in surveys) Proficiency in
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] Subject Areas: Applied Mathematics, numerical methods, simulation and modelling Appl Deadline: 2025/05/31 11:59PM (accepting applications posted 2025/02/13) Position Description: Position Description
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the DLR Institute of Communications and Navigation, offers a promising alternative by using medium-wave signals from existing maritime radio stations. The AIR-MoPSy project aims to improve the accuracy and
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policy. The Institute works on an interdisciplinary basis and is highly networked both nationally and internationally. About the position We are looking for a 100% postdoctoral research associate beginning
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, B., Holtkamp, E., et al. Integration of variant annotations using deep set networks boosts rare variant association testing. Nature Genetics (2024) Marconato, L. , Palla, G. , Yamauchi, K. A