Post Doc Fellow, A&S College of Arts and Sciences
Job in
Cincinnati, Hamilton County, Ohio, 45221, USA
Listed on 2026-07-16
Listing for:
Disability Solutions
Full Time
position Listed on 2026-07-16
Job specializations:
-
Research/Development
Postdoctoral Research Fellow, Data Scientist, Research Scientist
Job Description & How to Apply Below
Next Lives at the University of Cincinnati
Founded in 1819, the University of Cincinnati ranks among the nation’s best urban public research universities. Home to more than 53,600 students, 12,000 faculty and staff, and over 360,000 living alumni, UC, a Carnegie 1 institution, combines research prowess with a physical setting The New York Times has called “the most ambitious campus design program in the country." UC's momentum has never been stronger as the anchor of the Cincinnati Innovation District, the oldest cooperative education (co-op) program in the country with students earning $94 million annually through paid experiences, an academic health system, and as a member of the Big 12 Conference.
The university contributes $10.6 billion in economic impact to the city and $22.7 billion to the state of Ohio. At UC, next is all of us. Learn more .
UC is a mission-driven organization where we are committed to student success and positively transforming the community through scholarship and service. We thrive on innovation, making an impact, and fostering an environment where staff and faculty are key contributors to UC’s success.
Job Overview
The Experimental Neutrino Physics Group at the Department of Physics within the University of Cincinnati’s College of Arts & Sciences invites applications for a Postdoctoral Fellow position centered on data analysis using advanced fitting methods, and the development of state-of-the-art Deep Learning-based particle identification and reconstruction tools for the DUNE and NOvA neutrino experiments.
Research at the Experimental Neutrino Physics Group at the University of Cincinnati focuses on searches for Beyond the Standard Model (BSM) oscillations and advanced particle identification and reconstruction techniques. The group members hold leading roles on the DUNE and NOvA experiments and are contributing to the construction and installation of the DUNE Far Detectors through deployment of a Cold Electronics Quality Control Testing facility.
Further, the Department’s High-Energy Physics group has active programs in both experimental and theoretical research, with experimental efforts including the BELLE, BELLE II, and LHCb Intensity Frontier experiments, and theoretical efforts focused on flavor physics and dark matter.
The selected candidate will be expected to, under direct supervision, make substantial contributions to research on DUNE and NOvA, with potential opportunities including searches for sterile neutrinos on NOvA using novel statistical methods, and searches for tau neutrino appearance on DUNE using advanced machine learning reconstruction tools. The candidate will be expected to work with applications of machine learning algorithms and/or advanced statistical and computational methods to particle physics problems.
This position will be preferably based at the University of Cincinnati.
Essential Functions
- Duties will be focused on the Deep Underground Neutrino Experiment (DUNE) and the NuMI Off-Axis Electron Neutrino Appearance (NOvA) Experiment.
- Will conduct research in experimental particle physics centered on Data Analysis for the DUNE and NOvA neutrino experiments;
- Will take a leading role in the development of the PISCES fitting framework.
- Will take a leading role in the development of the NuGraph3 graph neural network.
- Assist in manuscript writing and presentation of research results.
Minimum Requirements
All of the following are required prior to effective appointment date:
- Doctoral Degree in Physics or a closely related area;
- Academic research background in analysis of Experimental High Energy Physics data sets.
- Familiarity with advanced statistical methods, especially using hybrid Poisson and multivariate Gaussian likelihoods, like the PISCES method.
Doctoral Degree in the appropriate field or discipline, or the recognized terminal degree
Required Trainings/Certifications
N/A
Required Experience
N/A
Physical Requirements/Work Environment
- Office environment/no specific unusual physical or environmental demands.
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