18 phd-agent-based-modelling Postdoctoral positions at University of Virginia in United States
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Research Associate position, with the possibility of extension based on an annual basis dependent upon satisfactory performance and the availability of funding. This position is part of the Preventive
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science of science, network science, and natural language processing. As part of a small research team, the postdoc will help lead efforts to provide a quantitative model of global competitiveness
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learning algorithms on graphs to model, characterize, predict, and design the thermal and physical behaviors of diverse material systems. Responsibilities also include the development of software codes
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autoimmune diseases. QUALIFICATIONS Applicants must have PhD and/or MD (or equivalent) degree in hand by start date. Preferred applicants will have experience in immunology, metabolism, immunometabolism, and
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cardiovascular treatments. In this role you will apply both machine learning predictive modelling and human genetic analyses of non-coding regulatory sequences to identify cell-specific targets for coronary artery
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on the potential survival of consciousness after death. Today, our broad mission is the scientific investigation of phenomena that challenge currently accepted models of the nature of mind and consciousness
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Research Associate position, with the possibility of extension for up to two years based on an annual basis dependent upon satisfactory performance and the availability of funding. The candidate will be
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will investigate the epigenetic regulation of HSV latency and reactivation. Multiple projects are available, based on strong preliminary data, and are funded by NIH grants. The projects will inform
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the development of novel therapies for coronary microvascular disease. The research program focuses on clinical translation and the development and validation of existing and novel cardiac imaging-based
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research using complex observational healthcare data, with a focus on cancer studies. The successful candidate will be expected to: Modeling multilevel survival data while addressing confounding and missing