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***REVISED*** Postdoctoral Research Associate - Computational HEP
AI / Machine Learning Researcher
***REVISED*** Postdoctoral Research Associate - Computational HEP at Princeton University – Princeton, Iowa
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About This Position
Princeton University: Office of the Dean of the Faculty: Natural Sciences: Physics
LocationPrinceton, Switzerland (CERN) or Fermi Lab, Chicago, Illinois
Open DateMar 11, 2026
Salary Range or Pay Grade Salary range PDRA: $65,000 - $70,000/year and ARS: $67,000 - $79,000/year
Deadline Dec 31, 2026 at 11:59 PM Eastern Time
Description
Princeton University is seeking one (or more) postdoctoral or more senior research associates to work with the High Energy Experiment group in the Princeton Physics Department on computational and artificial intelligence research in experimental High Energy Physics (HEP). HEP experiments advance the understanding of 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 decades. The resulting scientific data sets are among the largest in the world.
The successful applicant(s) is expected to play a leadership role in one or more of the computational HEP R&D projects being pursued by the Princeton group for the High Luminosity Large Hadron Collider (HL-LHC) and future collider concepts. These include research into highly performant data analysis systems, design and implementation of novel event reconstruction and L1 trigger algorithms, and the application of novel artificial intelligence and machine learning techniques to all aspects of HEP experiments. Princeton leads the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP, http://iris-hep.org/), which is developing innovative solutions to the computational and data challenges of the HL-LHC, which will collect data through the 2030's. Our research program also includes detector component simulation and data reconstruction algorithms of the key4hep software ecosystem being developed in the context of the future collider community (specifically Future Circular Collider (FCC) and muon collider concepts). The successful applicants 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). This position will be based at Princeton University or at CERN in Geneva, Switzerland or at Fermilab near Chicago, Illinois, subject to discussion with the principal investigator. The work location for this position is in-person, on campus at the selected location. The term of the appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility of renewal pending satisfactory performance and continued funding and meeting all requirements; those hired at a more senior rank may have multi-year appointments.
Equal Employment Opportunity Statement
Princeton University is an Equal Opportunity 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.
Pay Transparency Disclosure
The University considers factors such as (but not limited to) the scope and responsibilities of the position, candidate's qualifications, work experience, education/training, key skills, market, collective bargaining agreements as applicable, and organizational considerations when extending an offer. The posted salary range represents the University's good faith and reasonable estimate for a full-time position; salaries for part-time positions are pro-rated accordingly.
The University also offers a comprehensive benefits program to eligible employees. Please see this link for more information.