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Temporary Machine Learning Postdoc Jobs in Utah (NOW HIRING)

University of Utah

Post Doc Res Assoc

Salt Lake City, UT · On-site

$70K - $82K/yr

... Postdoctoral Research Associate will conduct applied and fundamental research in energy systems ... intelligence and machine learning to energy systems. The position will involve developing ...

Utah State University

Postdoctoral Fellow I

Logan, UT · On-site

$42K - $57K/yr

... a Postdoctoral Research Fellow under the recently awarded grant to investigate the mechanisms of ... Develop systems bioinformatics approaches including Machine Learning-based to decipher host ...

Utah State University

Postdoctoral Fellow I

Logan, UT · On-site

$42K - $57K/yr

... a Postdoctoral Research Fellow under the recently awarded grant to investigate the mechanisms of ... Develop systems bioinformatics approaches including Machine Learning-based to decipher host ...

University of Utah

Assistant

Salt Lake City, UT · On-site

... or machine learning are especially encouraged to apply. Candidates will be evaluated on their ... D. is required, and postdoctoral experience is highly desirable. The University of Utah offers an ...

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Temporary Machine Learning Postdoc information

What is a temporary machine learning postdoc?

A Temporary Machine Learning Postdoc is a fixed-term research position, typically held at a university or research institution, focused on advancing knowledge and techniques in machine learning. Postdoctoral researchers in this role work on specific projects, often collaborating with faculty, graduate students, or industry partners. The position is designed to provide advanced training and research experience after earning a PhD, usually lasting from several months to a couple of years. Temporary postdocs may contribute to publishing academic papers, developing algorithms, and mentoring students, while preparing for longer-term academic or industry careers.

What skills and qualifications are needed to thrive as a temporary machine learning postdoc?

To thrive as a Temporary Machine Learning Postdoc, you need a PhD in a relevant field, a solid grasp of machine learning theory, and strong programming skills (often in Python or R). Experience with tools such as TensorFlow, PyTorch, and high-performance computing environments, as well as a record of peer-reviewed research, is typically required. Strong analytical thinking, collaboration, and effective communication help you stand out in this research-intensive role. These skills are essential for advancing cutting-edge research, publishing impactful findings, and contributing to interdisciplinary projects.

What types of projects and collaborations can a temporary machine learning postdoc expect to engage in?

A Temporary Machine Learning Postdoc typically works on cutting-edge research projects, often contributing to ongoing studies or initiating novel investigations within the field. Collaboration is common, both within their immediate research group and with interdisciplinary teams, such as data scientists, domain experts, or industry partners. Postdocs may also mentor graduate students, present findings at conferences, and publish papers, gaining valuable experience that can lead to academic or industry roles. The environment is fast-paced and research-driven, offering opportunities for professional growth and expanding one's research portfolio.

What is the difference between Temporary Machine Learning Postdoc vs Data Scientist?

AspectTemporary Machine Learning PostdocData Scientist
CredentialsPhD in Computer Science, Data Science, or related fieldBachelor's or Master's in Data Science, Computer Science, or related field; often requires experience
Work EnvironmentAcademic or research institutions, labsCorporate, tech companies, startups
Employer & Industry UsageUniversities, research centersBusiness, technology, finance, healthcare
Search & Comparison IntentUnderstanding research-focused roles, academic opportunitiesIndustry roles, applied data analysis, business impact

The Temporary Machine Learning Postdoc is primarily research-oriented, often in academic or research settings, requiring a PhD. In contrast, a Data Scientist typically works in industry, applying data analysis and machine learning to solve business problems, often with a Bachelor's or Master's degree. Both roles involve machine learning skills but differ in environment, focus, and experience level.

What are the most commonly searched types of Machine Learning Postdoc jobs in Utah?

The most popular types of Machine Learning Postdoc jobs in Utah are:

What are popular job titles related to Temporary Machine Learning Postdoc jobs in Utah?

For Temporary Machine Learning Postdoc jobs in Utah, the most frequently searched job titles are:

What job categories do people searching Temporary Machine Learning Postdoc jobs in Utah look for?

The top searched job categories for Temporary Machine Learning Postdoc jobs in Utah are:

Temporary Machine Learning Postdoc jobs near you
Infographic showing various Temporary Machine Learning Postdoc job openings in Utah as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 73% Full Time, 23% Part Time, and 2% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution.

Postdoctoral Research Associate

Salt Lake City, UT • On-site

Kessler Foundation
51 - 200 employees
Learn more about Kessler Foundationexternal

Other

Posted 13 days ago

Job description

The Department of Radiology and Imaging Sciences at the University of Utah invites applications for a Postdoctoral Research Fellow position in the Brain Network Lab. The successful candidate will join a multidisciplinary research team that includes faculty from radiology, psychology, biomedical engineering, and related disciplines. The Brain Network Lab is dedicated to understanding structural and functional brain changes across the lifespan. This position will contribute to the Department of Radiology and Imaging Sciences’ growing research initiative focused on identifying the etiology and biomarkers of neurodegenerative disorders. Research projects will leverage unique longitudinal neuroimaging datasets available through the University of Utah and its collaborative research programs.

Responibilities

Current projects focus on healthy aging, neurodegenerative diseases, and neurodevelopmental conditions, including developmental disabilities. The postdoctoral fellow will lead and contribute to the development and application of advanced neuroimaging analysis pipelines, including:

  • Longitudinal structural MRI analysis
  • Resting-state functional MRI connectivity and network analysis
  • Individualized brain parcellation and precision neuroimaging approaches
  • Multimodal image processing, registration, and quantitative analysis
  • Biomarker discovery using large-scale longitudinal datasets
  • Training and Career Development

This position offers outstanding opportunities for multidisciplinary mentorship, scientific collaboration, and professional development. The fellow will work closely with investigators across multiple departments and will have access to extensive longitudinal imaging resources and computational infrastructure. Training will emphasize the development of independent research skills, grant writing, manuscript preparation, and career advancement toward academic, industry, or clinical research careers. Salary will be commensurate with experience and qualifications and consistent with current NIH postdoctoral stipend guidelines.

Minimum Qualifications Required
  • PhD (completed or anticipated before the start date) in Neuroscience, Biomedical Engineering, Psychology, Computer Science, Medical Physics, or a related field
  • Evidence of research productivity, including peer-reviewed publications
  • Strong quantitative and analytical skills
Preferred
  • Experience with neuroimaging analysis methods and software (e.g., FreeSurfer, FSL , AFNI , ANTs, Connectome Workbench)
  • Programming experience in Python, MATLAB , R, or related languages
  • Experience with image processing, registration, machine learning, or computational neuroscience methods
  • Interest in aging, neurodegenerative disease, developmental disabilities, or lifespan neuroscience
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