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Postdoctoral Research Associate in Computational High Energy Physics

Employer
Princeton University
Location
Princeton, New Jersey

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Position Type
Postdoc
Employment Type
Full Time
Institution Type
Four-Year Institution

Job Details


Princeton University

Princeton University is seeking one (or more) postdoctoral or more senior research associates to work with the Princeton Institute for Computational Science and Engineering (PICSciE) and the High Energy Experiment group in the Princeton Physics Department on computational research in experimental High Energy Physics (HEP). HEP focuses on understanding the elementary particles that are the fundamental constituents of matter and their interactions. Obtaining scientific results from these experiments requires complex software and computing systems, developed by international teams of researchers over the course of decades. The resulting scientific data sets are among the largest in the world. The postdoctoral research associate(s) will be part of the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP, http://iris-hep.org/about/), which is developing innovative solutions to the computational and data challenges of the High Luminosity Large Hadron Collider (HL-LHC), which will collect data starting in 2027 and continue into the 2030's. The position(s) will play a leadership role in one of several R&D projects, including research into highly performant data analysis systems, the application of novel machine learning techniques to HEP, and/or the implementation of other innovative event reconstruction algorithms. The postdoctoral research associate(s) will also have the opportunity to do their own research on the CMS experiment at the Large Hadron Collider at the European Laboratory for Particle Physics (CERN) in Geneva, Switzerland. For additional information, contact Dr. Peter Elmer (Peter.Elmer@cern.ch). Where these positions will be based is subject to negotiation. Current group members are based in Princeton University in Princeton, NJ, at CERN in Geneva, Switzerland, and at Fermilab near Chicago, IL. Appointments are initially for one year, with renewal possible based on satisfactory performance and funding. Applicants must apply online at https://www.princeton.edu/acad-positions/position/21021 and include a curriculum vitae, a one-page statement of research experience and interests, and a cover letter with the names and contact information of three references. The position is subject to the University's background check policy. Essential Qualifications: Ph.D. in either Experimental Particle Physics or a closely related field, or advanced degree in Computer Science or related field with a focus on applications Strong programming skills, in particular with Python and/or C++ Experience developing scientific or data science software applications such as those being developed by IRIS-HEP Strong interpersonal, oral, and written communication skills Able to work collaboratively with researchers, faculty, and staff from diverse backgrounds Preferred Qualifications: Ability to direct efforts of others within teams of various sizes Experience working in large, international scientific collaborations and delivering software in such contexts Experience with one or more of the following: data analysis of large scientific datasets, data science and/or machine learning tools, trigger/reconstruction algorithms for large high energy or nuclear physics detectors, software development for GPUs and other new architectures as well as related performance optimizations Applications will be reviewed on a rolling basis as they arrive, and all applications received by 1 September 2021, will receive full consideration. Princeton University is an equal opportunity/affirmative action employer and all qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status, or any other characteristic protected by law.

PI139361693

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