Department

PSD Enrico Fermi Institute: Fleming Group

About the Department

The Enrico Fermi Institute is an interdisciplinary research unit within the Division of Physical Sciences of the University of Chicago. The Institute's activities include the following: string theory and theoretical high-energy physics, experimental high-energy physics, theoretical astrophysics and cosmology, experimental particle astrophysics, infrared and optical astronomy, cosmic microwave background observations, general relativity, and gravitational waves, and cosmochemistry.

Job Summary

We are seeking a Researcher to work at the intersection of AI/ML, particle physics, and medical physics. The successful candidate will have expertise applying AI/ML in at least one of these areas — particle physics or medical physics — and a willingness to learn new methods and domains and to transfer approaches across them. The role centers on developing modular, acquisition-aware AI foundation models and applying them to large-scale scientific and biomedical imaging data, and is well suited to someone excited to work across disciplines and to contribute to open, reproducible computational resources.

A primary focus of the position is developing a modular, acquisition-aware, multimodal AI foundation model designed to be generic to diseases of the brain, learning shared representations across conditions rather than being tied to a single disease. The Researcher will help build the model, assemble and harmonize multimodal brain-imaging datasets, and run the benchmarking and cross-condition transfer experiments at the heart of the project. The Researcher will also contribute to the group’s broader program of developing AI/ML methods for particle physics and the modeling of physical systems — including deep-learning approaches to reconstruction, classification, and analysis of large-scale detector data — and will help transfer modeling advances between the physics and brain-health domains. This position provides research and technical support activities related to scientific research projects, and ensures compliance of research activities with institutional, state, and federal regulatory policies, procedures, directives, and mandates.

This position is expected to last approximately one year, with the possibility of extension based on funding.

Responsibilities

• Designs, implements, and trains components of the foundation-model architecture, including modality-specific encoders, cross-modal fusion, acquisition-aware conditioning, and prediction heads.

• Develops and applies self-supervised training objectives (e.g., masked signal modeling, cross-modal contrastive learning) and knowledge-distillation strategies from existing foundation models.

• Curates, preprocesses, and harmonizes multimodal data (structural and functional MRI, PET, EEG, and clinical/phenotypic measures) from established repositories such as ADNI, OASIS-3, NACC, and BSNIP, including acquisition-metadata handling and cross-site harmonization.

• Builds and runs rigorous benchmarking evaluations comparing the model against disease-specific, single-modality, and existing foundation-model baselines, including pre-specified metrics, ablation studies, and cross-cohort generalization tests.

• Implements and evaluates zero-shot and few-shot transfer of the model across brain conditions (e.g., Alzheimer’s disease, schizophrenia, ADHD) and characterizes shared computational signatures.

• Develops AI/ML methods for particle physics and the modeling of physical systems, including deep-learning and generative AI approaches to reconstruction, event classification, simulation and surrogate modeling, and analysis of large-scale detector and experimental data.

• Adapts and transfers modeling approaches between the particle-physics and brain-health domains, contributing to the group’s cross-domain foundation-model methodology.

• Contributes to open, reproducible research outputs, including the FAIR benchmark, versioned code releases, containerized pipelines, model cards, and documentation.

• Trains and mentors graduate and undergraduate students in AI/ML methods, data pipelines, and reproducible-research practices.

• Maintains technical and administrative support for the research project.

• Analyzes and maintains data. Conducts literature reviews. Assists with preparation of reports, manuscripts, and other documents.

• Installs, sets up, and performs experiments and computational workflows, interacting with students and other laboratory staff under the direction of the principal investigator.

• Performs other related work as needed.

Minimum Qualifications

Education:

Minimum requirements include a college or university degree in related field.

Work Experience:

Minimum requirements include knowledge and skills developed through < 2 years of work experience in a related job discipline.

Certifications:

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Preferred Qualifications

Education:

• Master’s degree in machine learning/AI, physics, applied mathematics, or a related quantitative field.

Experience:

• Demonstrated experience developing AI/ML and deep-learning methods, including model design, training, and evaluation.

• Background in medical physics research or biomedical imaging (e.g., MRI, PET, or related modalities), and/or experience developing AI/ML methods for particle physics or other large-scale scientific/detector data.

• Experience with neuroimaging data and standard preprocessing tools.

• Familiarity with foundation models, transfer learning, self-supervised learning, or knowledge distillation.

• Research experience.

 

Technical Skills or Knowledge:

• Strong programming skills and experience with modern deep-learning frameworks (e.g., PyTorch).

• Experience with high-performance or GPU computing environments.

• Familiarity with version control, containerization, and reproducible-research tooling.

Preferred Competencies

• Work independently and collaboratively within a multidisciplinary research environment.

• Communicate methods and results clearly, in writing and in presentations.

• Manage multiple tasks and priorities and follow projects through to completion.

• Contribute to open-source software and reproducible scientific workflows.

Working Conditions

• University research/computing environment.

Application Documents

• Resume/CV (required)

• Cover Letter (required)

The University of Chicago uses AI-assisted tools to streamline and augment some recruitment processes; however, AI is not used to make hiring decisions.

When applying, the document(s) MUST be uploaded via the My Experience page, in the section titled Application Documents of the application.

Job Family

Research

Role Impact

Individual Contributor

Scheduled Weekly Hours

37.5

Drug Test Required

No

Health Screen Required

No

Motor Vehicle Record Inquiry Required

No

Pay Rate Type

Salary

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FLSA Status

Exempt

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Pay Range

$50,000.00 - $53,000.00

The included pay rate or range represents the University’s good faith estimate of the possible compensation offer for this role at the time of posting.

Benefits Eligible

Yes

The University of Chicago offers a wide range of benefits programs and resources for eligible employees, including health, retirement, and paid time off. Information about the benefit offerings can be found in the Benefits Guidebook.

Posting Statement

The University of Chicago is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender, gender identity, or expression, national or ethnic origin, shared ancestry, age, status as an individual with a disability, military or veteran status, genetic information, or other protected classes under the law. For additional information please see the University's Notice of Nondiscrimination.

 

Job seekers in need of a reasonable accommodation to complete the application process should call 773-702-5800 or submit a request via Applicant Inquiry Form.

 

All offers of employment are contingent upon a background check that includes a review of conviction history.  A conviction does not automatically preclude University employment.  Rather, the University considers conviction information on a case-by-case basis and assesses the nature of the offense, the circumstances surrounding it, the proximity in time of the conviction, and its relevance to the position.

 

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