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*Strong publication record (relative to degree timing) *Collaborative spirit in interacting with postdoctoral and PhD researchers on the team *Interest in developing and applying Large Language Models (LLM
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on i) phylogenomic inference of hundreds of whole genomic data already available; and ii) investigating rates of evolution across the genome and their correlation with phenotypic traits across various
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research positions in the following fields:1. Microfluidic and Lab-on-Chip development in a multidisciplinary lab. Candidates should demonstrate track-records in microfluidics, Lab-on-Chip, and micro
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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$3,000 per year for conference travel and career development. Teaching Participation in teaching is not required, though fellows may guest lecture in courses, with guidance from Princeton mentor(s
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of whole genomic data already available; and ii) investigating rates of evolution across the genome and their correlation with phenotypic traits across various subdivisions of the squamate body plan
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revolutionize the understanding and advancement of human health by conducting interdisciplinary foundational research, developing and harnessing advanced computational approaches, and training the next generation
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-year appointments. In addition to the aforementioned project, the appointee will have opportunities to develop additional projects with members of Dr. Vélez's lab and/or maintain their ongoing work
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starting July 2025, and will remain open until excellent fits are found. The successful candidate will develop and apply computational approaches to chemical datasets, with artificial intelligence/machine
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technologies. The Pritykin lab (http://pritykinlab.princeton.edu ) develops computational methods for design and analysis of high-throughput functional genomic assays and perturbations, with a focus on multi