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aims to develop new foundations and technologies for reasoning and planning that leverage large language models (LLMs) and integrate them with complementary approaches, including program synthesis, and
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) to uncover patterns and mechanisms of animal influences on ecosystem properties and functioning. The Davies Lab have used unoccupied aerial vehicles to collect a large amount of LiDAR data spanning natural and
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this academic year. We’re looking for two types of predoctoral fellows: “Machine learning” track: you’re very skilled in machine learning, in particular large language models. Ideally, you would have a Masters in
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preferred, and this can take various forms: working with large model simulations or large data, compiled programming languages, algorithms, etc. The position is an especially good match for candidates
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collaborators in economics, clinical medicine, health policy, and epidemiology across Harvard. Projects include both experimental (RCT) and quasi-experimental research and involve the use of large administrative
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visualize large-scale biological datasets. You will be directly mentored by a postdoctoral fellow, Dr. Josh Tycko, providing exposure and advice for the research career track. You will benefit from the rich
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an active research lab with several large grants and participate in many aspects of the research process, including recruitment of children and adolescents into research, preparation of IRB applications, data
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biomedical research practices from a feminist theoretical perspective. Facility with data analysis is desired, but not required. PhD must be completed before the start of the postdoctoral fellowship (ideally
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machine learning methods for computational materials physics and chemistry. Projects include: 1. Scientific software engineering of machine learning potentials for large scale molecular dynamics. We
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communication and writing skills desired. The ideal candidate is an independent, solution-oriented thinker with a strong background processing very large data sets, applying analytical rigor and statistical