- Salary
- $27 – $30
- Location
- Hyde Park Campus, United States of America
- Workplace
- Onsite
- Type
- Full-time
- Department
- IT
- Education
- Certification
- Closing date
- Today
- Source
- Workday
Description
Department
PSD Astronomy & Astrophysics: Administration
About the Department
Job Summary
Responsibilities
- Develop and benchmark simulation-based inference algorithms (e.g., NPE) for strong gravitational lensing parameter estimation.
- Implement domain adaptation techniques to improve model robustness and transferability between simulated and real survey data.
- Generate and curate strong lensing simulations using tools such as lenstronomy or similar ray-tracing frameworks.
- Train and evaluate deep learning models on simulated datasets and validate performance on realistic or real observational data.
- Investigate and mitigate systematic biases arising from simulation-to-reality mismatches (sim-to-real gap).
- Collaborate with team members to integrate inference pipelines into end-to-end analysis workflows.
- Conduct literature reviews to stay current with advances in SBI, domain adaptation, and strong lensing science.
- Document code, experiments, and results clearly, maintaining reproducible research practices.
- Present progress regularly in team meetings and contribute to scientific publications or conference proceedings.
- Provides technical and administrative support for a research project.
- Collects and enters data. Assists in analyzing data. Assists with preparation of reports, manuscripts and other documents.
- Performs other related work as needed.
Minimum Qualifications
Education:
Minimum requirements include vocational training, apprenticeships or the equivalent experience in related field (not typically required to have a four-year degree).
Work Experience:
Certifications:
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Preferred Qualifications
Education:
- BSc or MSc degree in astrophysics, physics, computer science or related discipline.
Experience:
- Python programming for scientific applications.
- Experience with AI/ML algorithms.
Technical Knowledge Skills:
- Computer programming, particularly Python.
Preferred Competencies
- Excellent written and oral communication skills.
- Ability to work in a diverse group that includes students, postdocs, and senior scientists.
- Ability to juggle multiple tasks.
Working Conditions
- Office setting.
Application Documents
- Resume (required)
- Cover Letter (required)
- References (preferred)
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
Role Impact
Scheduled Weekly Hours
Drug Test Required
Health Screen Required
Motor Vehicle Record Inquiry Required
Pay Rate Type
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.
Benefits Eligible
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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