26 community-detection-cluster-phd Postdoctoral positions at Chalmers University of Technology
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postdoctoral position in data analysis, where you will apply machine learning techniques to understand how resistance genes spread and to help detect infections caused by resistant bacteria. The position is part
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microwave cavities We seek candidates with the following qualifications: PhD in Physics or neighbouring fields Excellent communication skills in written and spoken English Ability to program in high-level
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to work on topics at the intersection of applied probability and analysis. The group around Pierre Nyquist currently consists of three PhD students and is focused on questions in probability theory and
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institutes and industry partners play a key role in our work. Our group currently includes 1 Professor, 1 Senior Scientist, 4 Postdocs and 8 PhD students. Your research will focus on significantly reducing
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position is embedded in a vibrant research environment that includes several PhD students and postdoctoral researchers. The project is a close collaboration between the Computer Vision Group at Chalmers
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collaboration with the Multiscale Inorganic Materials group, both part of the Division of Energy and Materials at Chalmers . The two groups together comprise nine senior researchers and 27 PhD students and
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the following qualifications: PhD in Physics, Chemistry or Materials Science, you must hold a doctoral degree awarded no more than three years prior to the application deadline. * Experience from first-principles
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with the project’s Principal Investigator, and practical implementation of this research with the AIMLeNS team. The role also offers ample opportunities to mentor PhD students, supervise MSc projects
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facilities that are highly aligned with the goals of the project. Who we are looking for We seek candidates with the following qualifications: To qualify for the position of postdoc, you must have a PhD degree
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We are seeking a highly motivated and skilled Postdoctoral researcher with interdisciplinary expertise to develop risk assessment and mitigation models using Large Language Models (LLMs