172 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" research jobs at Harvard University
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diverse, inclusive community dedicated to alleviating suffering and improving health and well-being for all through excellence in teaching and learning, discovery and scholarship, and service and leadership
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postdoctoral fellow in Professor Susan Murphy’s Statistical Reinforcement Learning Group. Our research concerns sequential decision making in digital health, including experimental design and reinforcement
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for opportunities abroad. These grants present an excellent opportunity for recently minted scholars to deepen their expertise, to acquire new skills, to work with additional resources, and to make connections with
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. Cross-Disciplinary Fellowships (CDF) are for applicants with a Ph.D. from outside the life sciences (e.g. in physics, chemistry, mathematics, engineering or computer sciences), who have not worked in
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date to be determined. Basic Qualifications A PhD related to programming languages by the start date. Experience in machine learning and formal verification. Individuals with a demonstrated track record
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design (using computer-aided software). Special Instructions A cover letter and current CV are required as part of the application. SEAS is dedicated to building a diverse and welcoming community, and we
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We highly recommend reviewing our department website’s Faculty pages to learn more about our faculty, their labs, and their research interest before applying. Please apply through the ARIeS portal
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native peoples, or peoples of African, Asian, or Hispanic descent. The fellowship includes the requirement to teach one course per year (ideally in the fall term), to participate in a fellowship program
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to prioritize work and coordinate research protocols with lab members Excellent attention to detail and organization skills Interest in learning and strengthening existing skillsets Special Instructions We highly
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are desirable. We particularly encourage applicants with expertise in Multi-scale Modeling, Evolutionary Computation, Diffusion models, Reinforcement Learning. The successful candidate will work in a highly