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Machine Learning Astronomy Jobs in Illinois (NOW HIRING)

Senior BI Engineer (Hybrid)

Chicago, IL · On-site +1

$72K - $105K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Architect and enhance ETL/ELT pipelines using Astronomer Airflow and other modern orchestration ... our AI and machine learning-powered Colossus™platform. We serve non-prime consumers and ...

Senior BI Engineer (Hybrid)

Chicago, IL · On-site

$72K - $105K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Architect and enhance ETL/ELT pipelines using Astronomer Airflow and other modern orchestration ... machine learning-powered Colossus platform. We serve non-prime consumers and businesses alike ...

Machine Learning Astronomy information

What is machine learning astronomy?

Machine learning astronomy is the application of machine learning techniques to analyze and interpret astronomical data. This field combines computer science, statistics, and astronomy to automate tasks such as classifying celestial objects, detecting anomalies, and predicting astronomical events. With the increasing volume of data from telescopes and space missions, machine learning helps astronomers process and extract meaningful insights more efficiently. Researchers in this area develop algorithms that can learn patterns from vast datasets, leading to new discoveries and a deeper understanding of the universe.

What are some common challenges faced by professionals working in machine learning astronomy?

Machine learning astronomers often encounter challenges such as handling extremely large and complex datasets, ensuring data quality, and effectively preprocessing astronomical data to reduce noise and artifacts. Additionally, interpreting model results in a scientific context can be demanding, as it requires both technical expertise and domain knowledge. Collaboration with astronomers, data engineers, and software developers is essential to ensure that machine learning models are both accurate and scientifically meaningful.

What are the key skills and qualifications needed to thrive as a machine learning astronomer, and why are they important?

To thrive as a Machine Learning Astronomer, you need a strong background in astrophysics, statistical analysis, and programming (often with a PhD in a related field). Proficiency with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and astronomical data systems is essential. Critical thinking, problem-solving, and effective collaboration are key soft skills for innovating solutions and working within research teams. These skills enable the effective analysis of large astronomical datasets, driving new discoveries and advancements in the field.

What is the difference between Machine Learning Astronomy vs Data Scientist?

AspectMachine Learning AstronomyData Scientist
Required CredentialsDegree in Astronomy, Physics, or related fields; knowledge of machine learningDegree in Computer Science, Statistics, or related fields; strong programming skills
Work EnvironmentResearch institutions, observatories, academiaCorporate, tech companies, consulting firms
Industry UsageAnalyzing astronomical data, developing models for celestial phenomenaBusiness analytics, predictive modeling, data visualization

Machine Learning Astronomy focuses on applying machine learning techniques to astronomical data within research settings, while Data Scientists work across various industries analyzing data to inform business decisions. Both roles require strong analytical skills and programming knowledge but differ in domain focus and work environment.

How is machine learning used in astronomy?

Machine learning astronomy involves applying algorithms to analyze large datasets from telescopes and space missions, enabling tasks such as identifying celestial objects, classifying galaxies, detecting exoplanets, and predicting cosmic phenomena. Professionals in this field often use tools like Python, TensorFlow, and data analysis techniques to interpret complex astronomical data efficiently.

What cities in Illinois are hiring for Machine Learning Astronomy jobs?

Cities in Illinois with the most Machine Learning Astronomy job openings:

AI for Astrophysics Research Technician

University of Chicago Library

Chicago, IL

$27 - $30.77/hr

Full-time

Medical, Retirement, PTO

Posted 13 days ago


University Of Chicago rating

8.1

Company rating: 8.1 out of 10

Based on 47 frontline employees who took The Breakroom Quiz

163rd of 620 rated colleges and universities


Job description

Department

PSD Astronomy & Astrophysics: Administration


About the Department

The Department of Astronomy & Astrophysics is focused on teaching, research, and training in the modern field of astrophysics.


Job Summary

This position supports a research project at the intersection of astrophysics, cosmology, and artificial intelligence / machine learning (AI/ML), focused on strong gravitational lensing analysis using simulation-based inference (SBI) with domain adaptation techniques. The work centers on developing robust, transferable inference pipelines that bridge the gap between synthetic simulations and real observational data from large-scale astronomical surveys such as DES and LSST. The research technician will contribute to the design and implementation of deep learning methods aimed at addressing fundamental questions in cosmology, working with large datasets and modern AI/ML inference methods throughout the project.

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:

Minimum requirements include knowledge and skills developed through 2-5 years of work experience in a related job discipline.


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) MUSTbe 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

Hourly


FLSA Status

Non-Exempt


Pay Range

$27.00 - $30.77

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.

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.


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