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Field
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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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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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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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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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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
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, Chemistry or related scientific fields and experience and knowledge managing and analyzing spectroscopic data to build predictive models. The Successful candidates should be able to work independently, have
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computational science expertise. The Computational Science (CPS) Division focuses on solving the most challenging scientific problems through advanced modeling and simulation on the most capable computers
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real-world environments, fine-tuning AI models to classify qualitative data, and building generative AI pipelines to process multimodal data for downstream analysis. Applicants with experience in some
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including applications to biology , condensed matter analogues of axions , Condensed matter theory , Cosmology , General relativity , Phenomenology , Physics Beyond the Standard Model , Physics of cold atoms
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(that might predict learning and outcomes), underlying cognitive and neurobiological mechanisms, and intervention outcomes using tools including, but not limited to brain imaging (broadly defined