Research Specialist

• →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.

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

• 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.

• 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.

• 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.

• University research/computing environment.

• Resume/CV (required)

• Cover Letter (required)

​
FLSA Status

​
Pay Range

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.

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.

The University of Chicago's Annual Security & Fire Safety Report (Report) provides information about University offices and programs that provide safety support, crime and fire statistics, emergency response and communications plans, and other policies and information. The Report can be accessed online at: http://securityreport.uchicago.edu. Paper copies of the Report are available, upon request, from the University of Chicago Police Department, 850 E. 61st Street, Chicago, IL 60637.