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for sustainability in real-life settings; conduct action research and stakeholder engagement; work collaboratively in a team across disciplines and with multiple stakeholders; participate in conferences, workshops and
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Description Join us in seeking exciting new developments using graph theory in nearest neighbor models for active matter! Do you enjoy working with graph theory, and seeing how functions on graphs can inform
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framework to enable meta-analysis of multiple large biobank datasets, all of this with the aim of increasing our sensitivity to unravel the complex genetic causes of disease and, in so doing, identify new
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one of the following analysis techniques (multiple preferred): normative modelling, dimensionality reduction techniques, machine learning, deep-learning, state space modelling, advanced statistics
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Associate The Responsive Apparel Design (RAD) Lab at Cornell University is seeking a highly motivated and talented postdoctoral researcher to join a collaborative As part of Cornell University, a world-class
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. The research group led by Martin Enge is specialized in methodology-driven analysis of patient data, especially in the field of single-cell multiomics. We are a multidisciplinary group with expertise in both dry
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conversion of CO₂ into renewable fuels. Central to this effort is the investigation of advanced catalytic systems that bring together multiple functionalities, including components responsive to plasma
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aluminium through AI-driven microstructural analysis. About us The PhD candidate will work at the Division of Data Science and AI , in the neuro-symbolic research group. This group works with combinations
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Responsibilities for the role include: Data collection, cleaning, and merging from large-scale microdata sources (e.g., patents, dissertations, bibliometrics). Conduct data analysis using econometric and statistical
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and data analysis. Excellent interpersonal and communication skills. Task-related Competencies Design and prepare research materials and conduct analyses of the research findings. Work collaboratively