578 software-engineering-model-driven-engineering-phd-position positions at Yale University
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research uses both theory-driven models of cognition and neural activities and data-driven predictive modeling for precision medicine. In addition to our core research questions and mission, the unit will
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collaborative research combining functional models of acquired drug resistance with the clinical validation of novel circulating tumor DNA (ctDNA) assays. While the primary focus is wet lab experimentation
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. Qualifications Successful candidates should have a Ph.D. or MD/PhD in biomedical engineering, physics, electrical and computer engineering, computer science, applied mathematics or related fields. They should have
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Neuroscience Faculty Positions - Computational (2026-2027) Yale University: School of Medicine: Basic Science -YSM: Neuroscience Location New Haven, CT Open Date Aug 10, 2026 Deadline Nov 30, 2026
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communication skills. Application Process: Interested applicants should contact Dr. Mark Lee, MD, PhD ([email protected] ) with the Subject line: “Postdoctoral associate position in the Lee lab” with a brief
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to the causes and treatments of severe neuropsychiatric disorders and to serving as a model of exemplary mental health care for students and society. Overview of the Position: We are seeking multiple highly
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and mouse models of cerebrovascular disease Contribute to study design, interpretation of results, and preparation of manuscripts and presentations Required Qualifications PhD in molecular biology, cell
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prepare the postgrad student to design, execute, and interpret experiments to set them up for success in future independent research. This is an ideal position for someone looking to apply to PhD or MD/PhD
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, and human disease modeling. Qualifications Candidates should hold a bachelor’s degree in biology, neuroscience, genetics, molecular and cellular biology, biomedical engineering, computational biology
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future. This post-doctoral position will investigate the use of knowledge graphs on automatically extracted metadata at a cross-disciplinary global scale. Using LLMs and traditional data engineering