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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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Biodiversity Variables and combining these with environmental and biodiversity data to produce pan-European species distribution and habitat maps. You will apply machine-learning methods with
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and apply AI and machine-learning methods to infer interaction networks from acoustic co-occurrence and activity patterns, and analyse their structural properties to link topology to response diversity
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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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multitasked machine learning (MTL) framework for enzyme design. The work will also include improvement of the design with AI protein prediction followed by enhanced sampling simulations. Qualifications
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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 probabilistic machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models
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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 developing new image analysis and machine learning methods for precision medicine and clinical decision support? Would you like to work together with