44 machine "https:" "https:" "https:" "https:" "https:" "https:" Postdoctoral positions in Sweden
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Join us in designing stable materials for sustainable energy devices with machine-learning-accelerated simulation and modeling. Work assignments The postdoctoral researcher will develop machine
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. Beyond Discrete Mathematics, the Department of Mathematics and Mathematical Statistics carries out research in computational mathematics, financial mathematics, mathematical modeling, analysis, machine
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
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dynamics simulations and machine learning methods to study the structure and electrochemistry of disordered materials are also encouraged to apply. The project primarily aims to understand the complex
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, appointments of trust in trade union organizations, military service, or similar circumstances, as well as clinical practice or other forms of appointment/assignment relevant to the subject area. Applicants must
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subject area. The department has education assignments in engineering programs and master's programs. More information is available on our website . (https://kemi.uu.se/angstrom/?languageId=1 ). Work duties The
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. The postdoctoral researcher(s) will join an international research environment at Umeå University, including Stat4Reg (www.stat4reg.se ), which develops statistical and machine-learning methods for register data
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equivalent foreign degree, obtained within the last three years prior to the application deadline Experience with simulation frameworks, system-level performance evaluation, or machine learning, is highly
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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-facing web GIS dashboard. Investigate the forest, landscape, and climate conditions that drive storm susceptibility, using major windstorms as natural experiments and interpretable machine-/deep-learning