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Field
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: machine learning or deep learning, structural modeling and analysis, and genome or transcriptome analysis; have a strong record of peer-reviewed publications or equivalent scholarly output; collaborate
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conduct collaborative research between the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS, https://cosmos.uta.edu/ ) and the Department of Chemistry and Biochemistry (CHEM) on the topic
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the use of machine learning and AI approaches • Integration of proteomics with genetic data via MR, coloc and FUSION to identify causal and druggable targets Requirements • The successful applicant will
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demonstrable track record in studying humans and machine learning models, in the context of human social behaviour, learning, decision-making, or a related area. A proven track record of publishing work as lead
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clustering, redshift-space distortions, weak/strong gravitational lensing, and artificial intelligence/machine learning (AI/ML). The observational focus is on optical sky surveys (DES, DESI, Roman, Rubin Obs
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to test and compare strategies safely, calibrate models with real data, and support scenario-based decision-making. • Building data-driven models (e.g., forecasting, clustering/segmentation, learning-based
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machine learning within the Department of Electrical and Computer Engineering. The Postdoctoral Fellow conducts independent and collaborative research, publishes findings in peer reviewed conferences and
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to completion) or possess equivalent research experience in a relevant computational field such as data science, artificial intelligence, machine learning, computer science or statistics. They will bring strong
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industrial decarbonisation modelling to support the EU-funded FLARE project. The role will lead the technical development and integration of bottom-up, organisation-level decarbonisation models for energy
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on Nanoparticles You will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include: Advancing equivariant neural network