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Postdoctoral Fellow in Atmospheric Science. The successful candidate will conduct laboratory experiments investigating heterogeneous halogen and nitrogen chemistry on organic and biomass-burning aerosols under
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Genomics at Harvard Medical School Several positions are available in the Park Lab (https://compbio.hms.harvard.edu/ ). The aim of the laboratory is to develop and apply innovative computational methods
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these methods to address challenges in scientific discovery and precision medicine. We seek highly motivated applicants with a background in one or more of the following areas: agentic AI, geometric deep learning
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computational analysis of functional genomics datasets with an emphasis on single-cell omics. This person will develop new computational methods to analyze novel RNAseq, ATACseq, and Spatial Transcriptomics
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, computer science, or a related quantitative field for a two-year Postdoctoral Research Fellow position. This position involves developing statistical methods, data analytic tools, and mathematical models
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these methods to address challenges in scientific discovery and precision medicine. We seek highly motivated applicants with a background in one or more of the following areas: agentic AI, geometric deep learning
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quasi-experimental methods to identify causal effects and test the predictions of economic and sociological models. Examples of current research projects include: long-term impacts of neighborhoods and
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state of the art methods to relevant clinical questions. This project includes two aims: a) to assess the benefits and risks of weight loss treatments for pregnancy preparedness; and b) to assess whether
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quasi-experimental methods to identify causal effects and test the predictions of economic and sociological models. Examples of current research projects include: long-term impacts of neighborhoods and
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these methods to address challenges in scientific discovery and precision medicine. We seek highly-motivated applicants with background in one or more of the following areas: agentic AI, geometric deep learning