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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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. Researchers with prior experience using molecular dynamics simulations and machine learning methods to study the structure and electrochemistry of disordered materials are also encouraged to apply.The project
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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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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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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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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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comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures ( https://CGRlab.github.io/research/ ). About the research project We are open
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record of publications at leading venues in social science, machine learning, natural language processing, statistics, or computational social science is highly meritorious. You are a self-driven and