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simulations. (5) Interacting with other team members on industry-sponsored projects involving combustion, turbulence, multiphase flow, machine learning and artificial intelligence, algorithm development and
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and great opportunity of interdisciplinary training in machine learning and functional genomics. The project combines cutting-edge computational approaches, especially state-of-the-art machine learning
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Sequencing (GBS), Whole Genome Sequencing (WGS) and targeted genotyping chips (e.g. MassArray) and analyze them together with environmental data using population genetics and machine learning tools
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Qualifications: We seek exceptionally strong candidates with a PhD in statistics, biostatistics, computer science or a related field. Candidates with demonstrated accomplishment in academic research, as can be
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Purdue’s strategic focus on health equity and population health. Program Goals and Learning Objectives The fellow will gain experience in: Designing and conducting pharmacoepidemiologic studies in cancer and
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to diverse expertise and resources across leading institutions. The successful candidate will join a dynamic and collaborative research team dedicated to developing cutting-edge AI, machine learning, and
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the culture of the College of Liberal Arts (#ThinkBroadlyLeadBoldly). Pay band: In addition to annual salary of up to $75,000, this position provides office space, computer equipment, and $2500 in yearly
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addition to annual salary of up to $75,000, this position provides office space, computer equipment, and $2500 in yearly support for scientific conferences or professional development. A sign-on bonus will also be
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discussions for other timelines are possible. Qualifications: PhD in a broadly related field (e.g., cognitive science, computer science, psychology, learning sciences, linguistics, speech-language-hearing
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to learn new techniques and knowledge in imaging instrumentation and computational biophysics. Demonstrated ability to work in a team with interdisciplinary members, and to work independently with minimal