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Postdoctoral In Reinforcement Learning Jobs in Chicago, IL

Braze

Forward-Deployed Data Scientist II

Chicago, IL · Hybrid

$98K - $164K/yr

Skilled in SQL for querying/manipulating datasets, with experience in machine learning pipelines ... reinforcement learning algorithms * Customer collaborator: Comfortable working directly with ...

Braze

Senior Lead AI Solutions Consultant

Chicago, IL

$124K - $186K/yr

An expert in machine learning: You have a solid grasp of machine learning, including a familiarity with reinforcement learning * Mar-tech savvy: You understand the marketing technology ecosystem and ...

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Postdoctoral In Reinforcement Learning information

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$25.8K

$60.8K

$86K

How much do postdoctoral in reinforcement learning jobs pay per year?

As of Jun 23, 2026, the average yearly pay for postdoctoral in reinforcement learning in Chicago, IL is $60,801.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,500.00 and $68,500.00 per year, depending on experience, location, and employer.

What is the difference between Postdoctoral In Reinforcement Learning vs Postdoctoral In Machine Learning?

AspectPostdoctoral In Reinforcement LearningPostdoctoral In Machine Learning
Required CredentialsPhD in Computer Science, AI, or related field; strong programming skills; research experience in reinforcement learningPhD in Computer Science, AI, or related field; strong programming skills; research experience in machine learning
Work EnvironmentAcademic labs, research institutions, industry R&D teams focused on reinforcement learning applicationsAcademic labs, research institutions, industry R&D teams working on various machine learning techniques
Industry UsagePrimarily in AI research, robotics, gaming, and autonomous systemsBroader applications including data analysis, predictive modeling, and AI research

Postdoctoral In Reinforcement Learning specializes in research related to decision-making algorithms and autonomous systems, whereas Postdoctoral In Machine Learning covers a wider range of AI techniques. Both roles require similar credentials but differ in focus and application areas.

What are the key skills and qualifications needed to thrive as a Postdoctoral Researcher in Reinforcement Learning, and why are they important?

To thrive as a Postdoctoral Researcher in Reinforcement Learning, you need a PhD in computer science or a related field, with deep expertise in machine learning, statistics, and algorithm development. Proficiency in programming languages such as Python, experience with deep learning frameworks (e.g., TensorFlow or PyTorch), and familiarity with reinforcement learning libraries are typically required. Strong analytical thinking, problem-solving ability, collaboration, and scientific communication skills help you excel in research teams and publish impactful work. These competencies are vital to advancing state-of-the-art research, developing novel algorithms, and contributing to the academic and industrial progress in AI.

What are some common challenges faced by postdoctoral researchers in reinforcement learning, and how can they be addressed?

Postdoctoral researchers in reinforcement learning often face challenges such as balancing independent research projects with collaborative work, staying up-to-date with rapidly evolving literature, and managing the pressure to publish in top conferences. Effective time management, regular engagement with the research community through seminars and workshops, and seeking mentorship from senior colleagues can help address these challenges. Additionally, collaborating with interdisciplinary teams can offer fresh perspectives and support, making it easier to navigate complex research problems.

What is a Postdoctoral Researcher in Reinforcement Learning?

A Postdoctoral Researcher in Reinforcement Learning is an individual who has completed a PhD and conducts advanced research in the field of reinforcement learning, a branch of artificial intelligence focused on how agents take actions in environments to maximize rewards. These researchers often work in academic, industrial, or governmental research settings, collaborating on projects that advance the theoretical foundations or practical applications of reinforcement learning. Their responsibilities may include designing experiments, developing algorithms, publishing papers, and mentoring graduate students.
What are popular job titles related to Postdoctoral In Reinforcement Learning jobs in Chicago, IL? For Postdoctoral In Reinforcement Learning jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Postdoctoral In Reinforcement Learning jobs in Chicago, IL look for? The top searched job categories for Postdoctoral In Reinforcement Learning jobs in Chicago, IL are:
What cities near Chicago, IL are hiring for Postdoctoral In Reinforcement Learning jobs? Cities near Chicago, IL with the most Postdoctoral In Reinforcement Learning job openings:
Postdoctoral In Reinforcement Learning jobs near you
Infographic showing various Postdoctoral In Reinforcement Learning job openings in Chicago, IL as of June 2026, with employment types broken down into 5% Locum Tenens, 75% Full Time, 10% Part Time, and 10% Nights. Highlights an 89% Physical, 5% Hybrid, and 6% Remote job distribution, with an average salary of $60,801 per year, or $29.2 per hour.
Postdoctoral Appointee - Land-atmospheric interactions and hydrological extremes

Postdoctoral Appointee - Land-atmospheric interactions and hydrological extremes

Argonne National Laboratory

Lemont, IL

$72K - $121K/yr

Full-time

Posted 19 days ago

Job description

The Argonne National Laboratory at U.S. Department of Energy supported research programs invites applications for a Postdoctoral Research Associate position focused on understanding the relative role of land-atmosphere interactions in S2S predictability; impacts on boundary layer processes, aerosol-cloud interactions, precipitation, and hydrological extremes, including feedback mechanisms that influence the development, intensification, and persistence of extreme events, using observational datasets, machine learning, and Earth system modeling.

The successful candidate will work with observational and modeling teams using data from the Atmospheric Radiation Measurement (ARM) User Facility including the Bankhead National Forest (BNF) and Desert-Urban System Integrated Atmospheric Monsoon (DUSTIEAIM) observational datasets, together with advanced modeling frameworks such as Energy Exascale Earth System Model (E3SM) or data-driven AI models.

Position Requirements

  • Completed or soon-to-be-completed Ph.D. within the last 0-5 years in Atmospheric Science, Meteorology, Climate Science, Applied Mathematics, Data Science, or a related field with strong quantitative and computational research experience
  • Strong background in atmospheric dynamics, turbulence, land-atmosphere interactions, cloud physics, or precipitation processes
  • Experience working with large observational or model datasets
  • Experience with developing AI/ML Deep Learning models, working with agentic workflows, model training and other emerging AI techniques and tools
  • Programming experience in Python, C++, or similar scientific computing environments
  • Demonstrated ability to publish scientific research
  • Ability to model Argonne's Core Values: Impact, Safety, Respect, Integrity, and Teamwork

Job Family

Postdoctoral

Job Profile

Postdoctoral Appointee

Worker Type

Long-Term (Fixed Term)

Time Type

Full timeThe expected hiring range for this position is $72,879.00-$121,465.00.

Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

Click here to view Argonne employee benefits!

As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.

Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.

All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.

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