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(MSCA-PF) to pursue a career in research. We invite applications from promising young researchers within the field of Computational Biology & Ecological Modeling. The selected candidate will write a
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 2 months ago
to the weather prediction and climate projections. This is mainly due to our lack of understanding of cloud/snow ice microphysics and over-simplified representation in models. On a broader sense, although weather
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roughness, AM roughness is characterized by randomness, porosity, and powder adhesion, producing flow behaviors that existing correlations and turbulence models fail to predict. Understanding and modeling
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and integration of multimodal neuroimaging, behavioral and clinical data, and building large-scale deep learning models for multimodal neuroimaging datasets to construct predictive network models in
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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
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reputation and publication record in consciousness science, psychedelic science, predictive-processing, computational modelling, and integrative theoretical work. You will have a proven track record of
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in modeling and analysis of cyber-physical systems At the Technical Faculty of IT and Design, Department of Computer Science, a full-time postdoc position in modeling and analysis of cyber-physical
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Veterinary Medicine ● Variant discovery and genome annotation: Apply deep learning and graph-based models to improve variant calling, transcriptome annotation, and functional prediction in veterinary-relevant
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environmental modeling programs to review datasets to ensure compliance with QA/QC criteria, perform statistical analyses, fit model curves to data sets, predict contaminant fate and transport in natural and
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research communities. Applicants are invited to propose a research project around the development of AI models for predicting promising catalyst candidates to integrate molecular modelling techniques