26 mathematical-analysis-math-physics Postdoctoral positions at Chalmers University of Technology
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://www.chalmers.se/math/ At the division of Applied Mathematics and Statistics we conduct research within probability theory and its applications, the theory and implementation of finite element methods, inverse wave
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). This position offers a unique opportunity to collaborate closely with researchers across the Division of Marine Technology at Chalmers University, with a focus on maritime transportation risk analysis. Project
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work will involve both chemical analysis and mathematical modeling. In addition, you will be responsible for writing research papers and sharing your findings. You will also collaborate with partners
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manipulation, and metasurfaces. Who we are looking for We seek candidates with the following qualifications: The applicant should have a PhD degree in physics, optics, nanoscience, or a related subject area. The
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the research project and the division of Astronomy and Plasma Physics The successful candidate will work on developing and testing algorithms for 3D magnetic field reconstruction. The position will
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. Project overview The project involves applying advanced statistical analysis, machine learning techniques, and modeling approaches such as agent-based modeling to analyze diverse climate and socioeconomic
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processing Programming skills (e.g. Python or MATLAB) Statistical analysis A strong publication record * The date shown in your doctoral degree certificate is the date we use, as this is the date you have met
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journals and at conferences. Qualifications The candidate must hold a doctoral degree in Computer Science, Mathematics, Informatics, or other related disciplines, awarded no more than three years prior
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The applicant should have a PhD degree in transport, operations research, applied mathematics, computer science, or similar topics. Experience with optimization, data-driven or machine-learning skills
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applications, specifically targeting the prognosis and risk prediction of Heart Failure (HF) in patients. This research integrates AI safety, explainability, and multimodal medical data analysis to enhance