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functional characterization center. The lab is especially interested in non-coding cis-regulatory elements (CREs) and the variation within them, using high-throughput experimental approaches such as
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) consortium as well as part of an ENCODE functional characterization center. The lab is especially interested in non-coding cis-regulatory elements (CREs) and the variation within them, using high
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knowledge of reinforcement learning are also required. Familiarity with standard software development tools such as GitHub Workflow, servers, and AI coding tools is essential, as is a strong
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software development. Excellent coding skills, critical thinking, and a collaborative perspective are essential to this position. Training: Training under the supervision of Dr. Youngsun Cho, and the
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hands-on education in molecular genomics, population genetics, evolutionary theory, non-coding regulatory biology, and/or bioinformatics via their participation in research projects in the laboratory
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appointee will also learnthe fundamentals of clinical informatics and different medical data formats (DICOM ect). Training : The appointee will receive training in project-relevant coding languages
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modern ML architectures Experience with open-source software development and collaborative coding Familiarity with carbon markets Experience with Google Earth Engine Position Details Appointment: 1
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should have completed an MPH or an equivalent degree. The ideal candidate will have expertise in coding in R/RStudio, analysis of large data sets, prior experience with infectious disease modeling
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expected within 6 months. Demonstrated experience in in vivo two-photon imaging using calcium indicators in mice. Strong quantitative and analytical skills; prior coding experience (e.g., Python, MATLAB
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interface with the Linux-based supercomputing clusters at Yale, complete data processing, organization and analysis. They will learn to utilize R or related programs for analysis, graphing and coding