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projects that apply machine learning and advanced computational modeling to integrate multi-omics, clinical, and imaging data for biomarker discovery and mechanistic insights in AD. The position offers
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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
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. of Forestry & Natural Resources at Purdue University. The position will be jointly supervised by Drs. Matt Ginzel (Purdue) and Vikram Chhatre (USDA Forest Service) and will be based at the Hardwood Tree
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medication use Applying frameworks such as the Andersen Behavioral Model (ABM) and 4Ms of Geriatrics Conducting advanced data analysis and predictive modeling Engaging patients, caregivers, and health system
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opportunities in quantum science. We are also interested in optically manipulating collective properties of molecular crystals. Applicants should have a strong experimental background and a PhD in physics
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Lashkari's research group for 2+1 years (renewed annually) with the third year extension conditioned on availability of funding. Qualifications Applicants must hold a PhD in physics or a related field by the
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techniques including deep neural networks and large language models (such as GPTs), with state-of-the-art functional genomics approaches, including crop genomics, genome editing, and single cell multiomics
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. Qualifications Applicants must hold a PhD in physics or a related field by the time of appointment. Application Instructions To apply, please complete the application form on AcademicJobsOnline and submit
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strong overlap in the fields of topological quantum field theory, quantum condensed matter theory, and quantum computing and algorithms. Qualifications Applicants must hold a PhD in physics or a related