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Postdoctoral Research Opportunity: Lipid Biology, Cell Death, and Evolutionary Genomics. We are seeking a highly motivated Postdoctoral Associate to join our collaborative research program at the intersection
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collaborative research program at the intersection of lipid biology, cell death, and evolutionary genomics. The project, primarily led by the Atilla Laboratory (Department of Chemistry ), investigates how lipid
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are desirable. We particularly encourage applicants with expertise in Multi-scale Modeling, Evolutionary Computation, Diffusion models, Reinforcement Learning. The successful candidate will work in a highly
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: 13832 School: Faculty of Arts and Sciences Department/Area: Human Evolutionary Biology Position Description: The Department of Human Evolutionary Biology at Harvard University invites applications for a
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Details Title Postdoctoral Fellow within the Cavender-Bares Lab School Faculty of Arts and Sciences Department/Area Organismic and Evolutionary Biology Position Description A postdoctoral position
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-evolutionary dynamics of vegetation to geodynamics and even fewer investigate biogeodynamic interactions of tropical coastal wetland systems to evaluate the potential impacts of human-induced changes. We propose
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Expertise in molecular, cellular, or microbial biology. Experience with computational biology approaches, including analysis of large-scale sequencing data. Background and strong interest in evolutionary
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-scale Modeling, Evolutionary Computation, Diffusion models, Reinforcement Learning. The successful candidate will work in a highly collaborative and international environment. Contact Information
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of squamate reptiles; the largest group of terrestrial vertebrates on Earth today with 11,000 species. A Ph.D. in Evolutionary Biology, Computational Biology, or related fields, is required. The work will focus
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, located in the state-of-the-art Albert Sherman Center, is a forefront of research in Computational Biology, Evolutionary Biology, and Genomics. The Department focuses on deciphering complex biological data