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postdoctoral research associate to work with Professor Michael Desai at Harvard University on projects involving inferring sequence-function landscapes, using a combination of empirical data and ML methods (e.g
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to study the roles of microglia and oligodendrocytes in neurodegeneration and cognitive function. Analyze high-dimensional data to identify molecular pathways and targets relevant to Alzheimer’s disease and
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technologies. Experience with imaging technologies and computational data analysis preferred. Strong publication record and evidence of research independence. Excellent communication, teamwork, and problem
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and cell differentiation. We develop technologies to generate statistical data sets that make these problems tractable, as well as computational tools and laws governing biological system behavior. We
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sequence-function landscapes, using a combination of empirical data and ML methods (e.g. transformer models). One focus of this work will be on B-cell receptor evolution. Experience in applications of modern
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global cohorts. The postdoctoral research fellow will contribute to multiple research projects by engaging in the following activities: (1) ensuring adherence to data security and the responsible conduct
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measurements, analyze air quality data, and generate insights to support the commercialization of novel VOC detection technology. Key Responsibilities: · Conduct mobile field measurements using air sensors
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lab is focused on revealing the molecular and environmental rules governing cell fate decisions in development and cell differentiation. We develop technologies to generate statistical data sets
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volatile organic compound (VOC) exposure across diverse real-world indoor environments. This role offers the opportunity to conduct mobile sensor measurements, analyze air quality data, and generate insights
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Postdoctoral Fellow with Professor Morgane Austern. Professor Austern’s group focuses on research in high-dimensional statistics, probability theory, machine learning theory, graph data, Stein method, ergodic