52 multiple-sequence-alignment Fellowship positions at Harvard University in United States
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with BIG DATA (eg. NGS data in multi-TB scale). Experience using genome alignment software (bowtie2, bwa, tophat, etc.) is desired. Fluent in one programming language (Python, C, C++ or Java) and
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applications for multiple Postdoctoral Fellow positions working with Dr. Yi-Qiao Song, Prof. David Weitz, and Prof Donhee Ham on NMR/NQR/ESR technology development and applications in subsurface exploration and
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. Ideal candidates will have demonstrably strong research skills, evidenced by multiple publications in top-tier machine learning or artificial intelligence conferences and/or leading scientific journals
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across multiple institutions, access to cutting-edge computational resources, and a strong focus on translational impact. The successful candidate will work with Dr. Kohane to develop and receive research
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databases. Our team studies a range of interventions, from psychotropic agents to vaccines, in relation to multiple outcomes, from pregnancy losses to neurodevelopmental disorders in the infant. We apply
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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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for postdocs who are performing well and whose research contributions align with ongoing lab goals. Contact Information SB Hiring Team 200 Longwood Avenue Armenise Building 623 Boston MA, 02115 Contact Email
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will work with Dr. Bean and a team of postdoctoral researchers in the Bean lab to do early-stage drug discovery to find novel compounds to treat pain, epilepsy, and multiple sclerosis by modulating
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collaborators at Harvard and beyond, who bring their computational, data science, and biomedical expertise to our projects. Our work also benefits from multiple research software products (e.g.,HiGlass , Vitessce
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background. The ideal candidate will have existing expertise in several of the following areas, aligned with our research focus: 1) Causal inference, invariant learning and representation learning