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Field
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project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a collaboration between multiple research
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networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity rules which enable effective learning in large and deep networks and is consistent with
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to religious and worldview diversity for individuals in public service such as administration, health care, correctional facilities or the armed forces. The position's mandatory work (25%) will consist
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. Familiarity with computer programs necessary for academic research. Specific Requirements Excellent command of English and at least good command of one of the following languages: Italian, German, or French, as
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. The University of Nevada, Reno is committed to providing a place of work and learning free of discrimination on the basis of a person’s age (40 or older), disability, whether actual or perceived by others
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group (https://bckrlab.org). We focus on high impact applications and work on knowledge-centric AI and biomedical machine learning including multi-omics integration, single cell analysis, and sequential
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visualization. Experience with GWAS, Bayesian modelling, and/or machine learning applied to biological data. Strong programming skills (R, Python) and ability to manage large-scale -omics datasets. Good
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policies pertaining to other schools at Duke University. The postdoc candidate is expected to: 1) Develop novel methods for incorporating scientific machine learning in solving problems in solid mechanics
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: Geant4/TOPAS/GATE, etc.) Measurements – Treatment planning System. Prior Knowledge in Machine Learning algorithm is a plus. Supervisory Responsibilities No Required operation of university owned vehicles
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. The successful candidate will develop advanced machine learning (ML) models to automate and optimize retrosynthetic analysis, facilitating the discovery of efficient and sustainable synthetic routes for complex