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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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formulation, molecular-level characterization, and machine learning to develop predictive models linking chemical descriptors to adhesive properties. The work includes the development of a structured
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Postdoctoral position in machine learning for the development of novel semiconductor materials Login and apply Lund University Lund University was founded in 1666 and is repeatedly ranked among the
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Information Technology Are you interested in working with machine learning and simulation-based inference for searches for dark matter (or other “invisible” new physics signals) at the Large Hadron
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Information Technology Are you interested in working with probabilistic machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models
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evolving kinetic regimes. The work will combine experimental studies, mechanistic modelling, time-resolved data analysis, and machine learning to develop and validate predictive models linking process
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management, data analysis and data visualisation of data from clinical studies and registries using Python/R, with methods such as machine learning and network meta-analysis. Eligibility The
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molecular dynamics simulations, free energy methods, and machine learning). Candidates with either computational and/or experimental expertise are encouraged to apply. The postdoc will closely work with
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, hydraulics/hydrology, thermodynamics (e.g. heat transfer processes) - experience of sensor-based monitoring, field sampling, machine learning, programming as well as working with large environmental
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contribute to its shared infrastructure. Experience of machine learning, automated text analysis, historical census or register data, occupational classifications, comparative research, or research