90 genetic-algorithm-computer "Integreat Norwegian Centre for Knowledge driven Machine Learning" Postdoctoral positions at Stanford University
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not required as part of this position. Required Qualifications: Strong mathematical background, including expertise in one or more of the following areas: machine learning, statistics, and algorithms
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embryos This Human Frontier Science Program (HFSP) (link is external) funded project is in collaboration with the labs of Hervé Turlier (CIRB-CNRS) and Chema Martin (Queen Mary University of London). We
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. Xiaojie Qiu (Genetics & Computer Science) (link is external) and Dr. Matteo Molè (Obstetrics & Gynecology) (link is external) . Our goal is to explore the “black box” of early human pregnancy by mapping
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Posted on Mon, 11/11/2024 - 12:40 Important Info Faculty Sponsor (Last, First Name): Qiu, Xiaojie Stanford Departments and Centers: Genetics Computer Science Postdoc Appointment Term: Initial 2
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disparities, progression, and pathophysiology in HS, as well as other dermatologic conditions. Kavita Sarin, M.D./Ph.D., is a Professor of Dermatology and is the Director of the Stanford Skin Cancer Genetics
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Prevention Research Center, an interdisciplinary research program on the prevention of chronic disease, is seeking MD, PhD, and other post-doctoral level applicants for research fellowships. Fellows gain
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to known toxicants leading to vascular diseases such as coronary artery disease. We are seeking a highly motivated postdoctoral research candidate with a background in molecular biology, genetics, and/or
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collaborative culture. The Division of Pain Medicine is at the forefront of innovation in pain research, education, and patient care. Our postdoctoral program has successfully transitioned fellows
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for (1) engineering BPAN genetic variants into cell lines; (2) developing cellular assays including of autophagy function and cellular survival; (3) working with the Stanford high throughput drug screening
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. This includes integrating LLMs with structured data sources to develop robust computational phenotyping algorithms and scalable models for real-world evidence generation. The role will involve both method