25 apply-"https:"-"https:"-"https:"-"https:"-"https:" Postdoctoral positions at Harvard University
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eligible to apply. Deadline Month: April Description: Supports early career scholars working to bring transformative medical research, education, and technology to rid the world of neurological diseases
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to ambitious research at the intersection of machine learning, neuroscience, and computational biology. This role centers on computational neurobiology and the use of modern AI/ML methods to model brain circuits
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, such as leptin, have been deeply studied, the majority remain poorly characterized because existing tools are inadequate to study the entire secretome. For this project, we will use newly developed
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impact. Our team leverages data pipelines to quantify data centers’ electricity and water use, emissions, and air pollution exposure and health impacts. The overarching goal is to develop an interactive
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the Dana Foundation are not eligible to apply Deadline Month: May Description: The Dana Foundation accepts applications for professional development awards for trainees interested in opportunities in
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possible. Basic Qualifications PhD in computer science, statistics, electrical engineering, applied mathematics, computational biology, or a related quantitative field required by the expected start date
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academic careers. Applicants who are early career investigators with a Harvard affiliation and international investigators with a Harvard mentor are eligible to apply.
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AI/ML and a strong record of research accomplishment who are excited to develop new AI approaches for high-impact problems in cellular and protein computational biology. This role focuses on applying
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—are intended to foster the early career development of researchers who have transitioned or are transitioning from training environments in the physical/mathematical/computational sciences or engineering into
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-time Postdoctoral Research Fellow to join the causal inference team supervised by Professor Francesca Dominici. The position will focus on developing and applying novel causal inference methods for large