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.

This position is not eligible for sponsorship of an H-1B visa requiring consular processing; other visa sponsorships (including H-1B visas not requiring consular processing) may be available, as appropriate.

Essential Qualifications:

• Ph.D. in Experimental Particle Physics or a closely related field (Research or Scientific Computing Software, Computational Science, Data Science, and Machine Learning), or advanced degree in Computer Science or a related field with a focus on applications
• Strong programming skills, in particular with Python and/or C++
• Experience with AI/ML techniques and tools including model training and validation
• Experience with one or more of the following: data analysis of large scientific datasets, 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
• Experience developing scientific software applications for use by large scientific collaborations
• Strong interpersonal, oral, and written communication skills
• Ability 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 AI/ML model deployment

Applications must include a curriculum vitae, a one-page statement of research experience and interests, a cover letter, and three confidential letters of recommendation. The position is subject to the University's background check policy.

Applications will be reviewed on a rolling basis as they arrive, and all applications received by May 31,2026 11:59 (EST), will receive full consideration.