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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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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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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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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 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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Requirements REQUIRED: PhD in molecular biology, cell biology, genomics, biophysics, or any closely related field. Experience in chromatin is desired not required. The candidate is expected to have excellent
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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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environmental data to uncover the impact of early life experiences on long-term mental health. You can learn more about the Lab’s work on baby teeth on our website: www.teethforscience.com As a Postdoctoral
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. Assist in the mentorship of junior lab members ? Learn transferrable skills to become a successful PI Act as a subject matter expert for the team in DNAm, biomarkers of mental health risk, and statistical