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will develop novel machine learning and artificial intelligence (ML/AI) methods for genomics data, especially: large-scale single-cell genomics data, high-definition spatial genomics, digital pathology
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models to characterize agricultural and ecological systems; Experience in applying advanced Artificial Intelligence/Machine Learning (AI/ML) methods in agriculture (Agro-AI/ML); and Experience in
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lab’s website https://sites.duke.edu/corinnelinardiclab/ . Be You Work Performed · Perform literature reviews to guide research · Design and execution of standard in vitro assays ongoing in the lab
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associate or bachelor’s degrees through a combination of in-person, online or blended learning. All of our system institutions place strong emphasis on service — helping to build healthier, more educated
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interdisciplinary and experiential learning to join the Source Project , Binghamton University's distinctive first-year research program in the Humanities and Social Sciences. The position begins August, 2026. PhD in
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conferences and workshops. The Research Assistant Professor may be asked to teach (or may ask to teach) but that is neither required nor guaranteed. Qualifications Applicants must have a Ph.D. in Economics or
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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine
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perform routine logging and/or testing of samples. May occasionally instruct others in basic laboratory techniques. Working Conditions: Work is performed on-site in Cambridge, MA. May be required to work
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for this position, however, we are still accepting applications from other qualified candidates. Position Summary Our goal is to identify computational principles underlying learning and decision-making behavior and
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cytometry). 25%: Project Management Mouse colony and research project management. 15%: Data Collection and Distribution Utilize basic computer skills to log data, conduct routine computer analysis of research