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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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the theory of quantum graph states. Additional expertise in computational methods would be useful but is not necessary. The Postdoctoral and Senior Research Associate positions will also involve
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of the Postdoc Research Fellows are the following: Research: Work on novel AI/Data Science research with crucial interdisciplinary scope using machine/deep learning, generative/agentic AI, and knowledge graphs
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learning, small data learning · Active learning, Bayesian deep learning, uncertainty quantification · Graph neural networks This position involves active participation in a well-funded
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publications and presentations. Collect, analyze and graph data, conclude research projects in a timely manner, write reports, and manuscripts. Engage in career development activities, apply for dedicated
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of sparse matrix, tensor and graph algorithms on distributed and heterogenouscomputational environments. Basic Qualifications: A PhD in Computer Science, Applied Mathematics, Computational Science, or related
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career in research or academia. Participate in the University’s Individual Development Plan policy for postdoctoral scholars, have a PhD or equivalent terminal degree (such as MD, DVM, PsyD, PharmD, etc
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%). • Contribute to data and lab management (5%) Qualifications Required Qualifications: • PhD, DO, MD or similar degree in health sciences or related field • 3+ years experience in biological sciences laboratory
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preliminary analysis of the data using graphs, charts or tables to highlight the key points of the research results collected in accordance with the research protocols as stipulated. Prepare and present
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experience and enhanced potential to receive an ERC Starting Grant in the future. Open to both PhD (natural sciences) and MD (medical sciences) holders. From a variety of academic backgrounds: molecular