Research - Laboratory/Non-Laboratory, Staff/Administrative
Department: Physics and Astronomy
The Astroparticle Group of Rice University is seeking a postdoctoral researcher to conduct research using the XENONnT Dark Matter experiment as part of a new interdisciplinary NSF project to develop science-informed machine learning and data science algorithms for the physical sciences. The researcher would be responsible for implementing and assessing the performance of new algorithms on XENONnT data related to the dark matter and neutrinoless double-beta decay science cases.
The researcher will interface with machine-learning research groups from Delaware and Rutgers to aid development of novel methods, including but not limited to bayesian networks, semi-supervised learning, inverse problem formulations, and random-projection dimensionality reduction. The researcher will be responsible for deploying and iterating such new algorithms within XENONnT, specifically within the Analysis Tools working group. Therefore, experience with e.g. position and energy reconstruction in particle physics experiments is advantageous as data science performed in physical sciences is different than in computational sciences (for example, physics accepts an algorithm based on knowledge of statistical properties of a distribution rather than per-event loss functions).
Position Status: Full-Time
Rice University is an Equal Opportunity Employer with commitment to diversity at all levels, and considers for employment qualified applicants without regard to race, color, religion, age, sexual orientation, gender identity, national or ethnic origin, genetic information, disability or protected veteran status.
Internal Number: 20856
About Rice University
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