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Causal Inference Machine Learning Postdoctoral Jobs in Chicago, IL

Ulta Beauty

Data Scientist

Bolingbrook, IL · On-site

$102K - $130K/yr

... science and machine learning concepts (e.g., multivariate regression, statistical inference ... Understanding of causal and uplift models * Prior work in retail, e-commerce, or consumer-facing ...

Tavern Research

Data Scientist I

Chicago, IL · On-site

$95K - $113K/yr

Learn and apply best practices and emerging tools in machine learning, AI, and causal inference. Qualifications We know it's rare to check every box. If you meet most of these, we encourage you to ...

Ulta Beauty, Inc.

Data Scientist

Bolingbrook, IL

$102K - $130K/yr

... science and machine learning concepts (e.g., multivariate regression, statistical inference ... Understanding of causal and uplift models * Prior work in retail, e-commerce, or consumer-facing ...

Ulta Beauty, Inc.

Data Scientist

Bolingbrook, IL · On-site

$102K - $130K/yr

... science and machine learning concepts (e.g., multivariate regression, statistical inference ... Understanding of causal and uplift models * Prior work in retail, e-commerce, or consumer-facing ...

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All JobsCausal Inference Machine Learning Postdoctoral JobsCausal Inference Machine Learning Postdoctoral Jobs in Chicago, IL

Causal Inference Machine Learning Postdoctoral information

See Chicago, IL salary details

$36.6K

$55.9K

$62.8K

How much do causal inference machine learning postdoctoral jobs pay per year?

As of Jul 15, 2026, the average yearly pay for causal inference machine learning postdoctoral in Chicago, IL is $55,858.00, according to ZipRecruiter salary data. Most workers in this role earn between $55,100.00 and $58,200.00 per year, depending on experience, location, and employer.

What is a Causal Inference Machine Learning Postdoctoral researcher?

A Causal Inference Machine Learning Postdoctoral researcher is a scientist who specializes in developing and applying machine learning methods to understand cause-and-effect relationships in data. They typically hold a recent PhD in statistics, computer science, economics, or a related field, and work in academic or industry research settings. Their work involves designing experiments, analyzing complex datasets, and creating models that can infer causal relationships, which are crucial for making robust predictions and informed decisions. This role often collaborates with interdisciplinary teams to apply these techniques to domains such as healthcare, social science, or economics.

What are the key skills and qualifications needed to thrive as a Causal Inference Machine Learning Postdoctoral researcher, and why are they important?

To thrive as a Causal Inference Machine Learning Postdoctoral researcher, you need a strong background in statistics, causal inference methodologies, and advanced machine learning, usually evidenced by a PhD in a relevant field. Familiarity with programming languages such as Python or R, experience using statistical software (e.g., TensorFlow, PyTorch, Stan), and knowledge of causal inference libraries are typically required. Outstanding analytical thinking, problem-solving abilities, and strong communication skills help you collaborate effectively and explain complex concepts to diverse audiences. These skills and qualifications are vital for advancing research, deriving actionable insights from data, and contributing to impactful scientific discoveries.

What are some common challenges faced by Causal Inference Machine Learning Postdoctoral researchers when integrating causal models with real-world data?

Causal Inference Machine Learning Postdoctoral researchers often encounter challenges such as dealing with unobserved confounding variables, ensuring data quality, and addressing biases inherent in observational datasets. Integrating advanced machine learning techniques with causal inference frameworks requires careful consideration of model assumptions and validation methods. Collaboration with domain experts is essential to properly interpret results and to translate findings into actionable insights, especially in interdisciplinary settings like healthcare or social sciences.

What is the difference between Causal Inference Machine Learning Postdoctoral vs Data Scientist?

AspectCausal Inference Machine Learning PostdoctoralData Scientist
Required CredentialsPhD in statistics, machine learning, or related fieldBachelor's or Master's in data science, computer science, or related field
Work EnvironmentAcademic research, research labs, universitiesCorporate, tech companies, startups
Industry UsageResearch, academia, specialized industry projectsBusiness analytics, product development, data-driven decision making
Common Search/ComparisonYesYes

The main difference is that Causal Inference Machine Learning Postdoctoral roles focus on academic research and developing new methods in causal inference, often requiring a PhD. Data Scientists typically work in industry, applying existing models to solve business problems, with a focus on data analysis and visualization. While both roles involve machine learning, the postdoctoral position emphasizes research and theory, whereas data science emphasizes practical application.

What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in Chicago, IL? For Causal Inference Machine Learning Postdoctoral jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Chicago, IL look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Chicago, IL are:
What cities near Chicago, IL are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities near Chicago, IL with the most Causal Inference Machine Learning Postdoctoral job openings:
Causal Inference Machine Learning Postdoctoral jobs near you
Postdoctoral Appointee - AI for Synchrotron Imaging

Postdoctoral Appointee - AI for Synchrotron Imaging

Argonne National Laboratory

Lemont, IL • On-site

$72K - $121K/yr

Full-time

Re-posted 18 days ago

Job description

Position Overview
We are seeking a Postdoctoral Appointee to join the Computational Science and Artificial Intelligence Group in the X-ray Science Division of the Advanced Photon Source (APS) at Argonne National Laboratory to advance learning-enabled imaging methods. This position offers a unique opportunity for candidates with backgrounds in electrical engineering, computer science, applied mathematics, or physics to apply their expertise to challenging problems in computational imaging, while collaborating with leading experts in physics, biology, and environmental science.
Research Context
Soil microbial communities play a fundamental role in carbon and nutrient cycling, yet their spatial organization and interactions have remained difficult to study because of the opacity and complexity of soil. The APS at Argonne National Laboratory is a world-leading synchrotron facility recently upgraded to deliver nanometer-to-micron resolution imaging with dramatically increased X-ray flux. This makes it possible to visualize the interplay of soil structure and microbial life at scales bridging nanometers to millimeters, creating a unique opportunity to investigate how microbial communities are organized and interact within their natural environments.
Your Role
This position focuses on developing learning-enabled imaging methods to guide data collection and analyze synchrotron datasets, spanning the full experimental cycle from real-time X-ray measurements to post-experiment reconstruction:
  • 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.

Success in this role will require creativity in computational imaging, machine learning, and signal processing, as well as close collaboration with experts in computational science, electrical engineering, synchrotron physics, soil microbiology, and environmental chemistry. May be required to perform other duties as assigned.
Position Requirements
  • 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 Knowledge, Skills, and Experience
  • 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).

Job Family
Postdoctoral
Job Profile
Postdoctoral Appointee
Worker Type
Long-Term (Fixed Term)
Time Type
Full time
The 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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