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Internship Machine Learning Postdoc Jobs in Chicago, IL

Our internship is designed for curious problem-solvers who enjoy continuously learning. Through a combination of structured education, market simulations, and exposure to modern research and AI tools ...

Our internship is designed for curious problem-solvers who enjoy continuously learning. Through a combination of structured education, market simulations, and exposure to modern research and AI tools ...

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Internship Machine Learning Postdoc information

See Chicago, IL salary details

$26.3K

$43.9K

$90.7K

How much do internship machine learning postdoc jobs pay per year?

As of Aug 17, 2026, the average yearly pay for internship machine learning postdoc in Chicago, IL is $43,867.00, according to ZipRecruiter salary data. Most workers in this role earn between $33,500.00 and $47,400.00 per year, depending on experience, location, and employer.

What are the most commonly searched types of Machine Learning Postdoc jobs in Chicago, IL?

The most popular types of Machine Learning Postdoc jobs in Chicago, IL are:

What cities near Chicago, IL are hiring for Internship Machine Learning Postdoc jobs?

Cities near Chicago, IL with the most Internship Machine Learning Postdoc job openings:

Infographic showing various Internship Machine Learning Postdoc job openings in Chicago, IL as of August 2026, with employment types broken down into 50% Internship, and 50% Full Time. Highlights an 75% In-person, and 25% Remote job distribution, with an average salary of $43,867 per year, or $21.1 per hour.

Postdoctoral Appointee - AI for Synchrotron Imaging

Argonne National Laboratory

Lemont, IL • On-site

$49K - $67K/yr

Full-time

Re-posted 20 days ago


Job description

Job Summary:
Argonne National Laboratory is a leading research institution focused on advanced scientific discovery, and they are seeking a Postdoctoral Appointee to join their Computational Science and Artificial Intelligence Group. This role involves developing learning-enabled imaging methods for synchrotron datasets, collaborating with experts across various scientific domains to enhance understanding of microbial communities within soil.
Responsibilities:
• Develop learning-enabled algorithms for 3D reconstruction of noisy and heterogeneous synchrotron datasets.
• Implement adaptive acquisition strategies that guide beamline measurements in real time to increase efficiency and improve image quality.
• Advance multimodal analysis methods that align and fuse structural, chemical, and biological signals to construct coherent models of microbial organization across scales.
Qualifications:
Required:
• Ph.D. completed in the past 5 years or soon-to-be completed in Electrical Engineering, Computer Science, Applied Mathematics, Physics, or a related field.
• Strong expertise in machine learning, computational imaging, computer vision, or signal processing.
• Proficiency in scientific programming and modern ML frameworks, with the ability to implement and debug research-grade algorithms.
• Demonstrated ability to work on complex data analysis problems and deliver robust computational solutions.
• Excellent communication skills and a strong interest in interdisciplinary collaboration.
• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.
• Interpersonal skills, oral and written communication skills, and ability to interact with people at all levels both within and outside the laboratory.
Preferred:
• Experience with synchrotron or tomographic imaging datasets.
• Background in inverse problems or physics-informed machine learning.
• Exposure to scientific imaging applications (for example, biological, environmental, or materials science).
Company:
Argonne National Laboratory conducts researches in basic science, energy resources, and environmental management. Founded in 1946, the company is headquartered in Lemont, USA, with a team of 1001-5000 employees. The company is currently Late Stage.