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Field
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the construction of PRS and enhance disease prediction. Students will gain experience in: Statistical genetics and GWAS methodology Machine learning approaches for high-dimensional data Algorithm development and
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-solving person with a strong background in bioinformatics and previous experience with next-generation sequencing (NGS), ideally in cancer genetics. In particular, familiarity with the analysis of whole
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RAP opportunity at National Institute of Standards and Technology NIST Machine Learning for Autonomous Genetic Engineering of Microbial Systems Location Material Measurement Laboratory
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well as bioinformatics. Preferred or Related Degrees: Biology / Environmental Sciences Preferred or Related Master’s Degrees: Master’s in Fundamental Biology, Genetics and Evolution, Conservation Biology, or similar
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interactions, nuclear structure and reactions, electroweak structure, and lepton-nucleus scattering. The candidate will contribute to advancing statistical and computational algorithms to extend the capabilities
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review. You will possess master level quality, accuracy, speed and knowledge for your assigned specialist area and execute reflex testing algorithms. Work schedule: 100% FTE, Fixed Duration Appointment
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are pioneering discoveries across diseases that have the deepest impact on our community. THE OPPORTUNITY We’re seeking a motivated Statistical Genetics Postdoc (3-year role) to play a key role in our large-scale
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identification of mechanical parameters using genetic algorithms. The project lies at the interface between applied mechanics, micromechatronics and bioengineering, with potential applications in mechanobiology
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, bioinformatics pipeline development, and computational analysis of large-scale biobank datasets to study cardiovascular disease genetics. Primary responsibilities include analyzing common and rare genetic
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possible for up to 1 day/week. You will join an interdisciplinary team of researchers spanning imaging science, machine learning, genetics, and population health, working closely with collaborators