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Temporary Machine Learning Postdoc information

What are the key skills and qualifications needed to thrive as a Temporary Machine Learning Postdoc, and why are they important?

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 during their appointment?

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 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 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 Austin, TX? The most popular types of Machine Learning Postdoc jobs in Austin, TX are:
What are popular job titles related to Temporary Machine Learning Postdoc jobs in Austin, TX? For Temporary Machine Learning Postdoc jobs in Austin, TX, the most frequently searched job titles are:
What job categories do people searching Temporary Machine Learning Postdoc jobs in Austin, TX look for? The top searched job categories for Temporary Machine Learning Postdoc jobs in Austin, TX are:
What cities near Austin, TX are hiring for Temporary Machine Learning Postdoc jobs? Cities near Austin, TX with the most Temporary Machine Learning Postdoc job openings:
Temporary Machine Learning Postdoc jobs near you
Infographic showing various Temporary Machine Learning Postdoc job openings in Austin, TX as of May 2026, with employment types broken down into 100% Full Time. Highlights an 50% In-person, and 50% Hybrid job distribution.

IFML Postdoctoral Fellowship

The University of Texas at Austin

Austin, TX • On-site

$48.60K - $65.90K/yr

Full-time

Posted 12 days ago

University Of Texas at Austin rating

8.1

Company rating: 8.1 out of 10

Based on 62 frontline employees who took The Breakroom Quiz

128th of 528 rated colleges and universities

Job description

Description
The NSF AI Institute for Foundations of Machine Learning (IFML), and the NSF TRIPODS program at the University of Texas seek highly qualified candidates (within five years of the award of their PhD) for a new UT ML Research Fellow Program. Appointments will begin Summer or Fall 2024.
This multi-year program will host several postdoctoral researchers working on either:
(a) foundational problems in machine learning, optimization, and statistics and their relationship to algorithmic and methodological improvements for training and deploying ML models or
(b) problems that advance the state of the art in central use-cases of large scale ML: video, imaging, and navigation or some combination of the above topics or
(c) deep learning and protein biologics, especially protein engineering and applications of large-scale tools such as AlphaFold (we encourage candidates with PhDs in biology, chemistry, biochemistry or related fields with a background in computation to apply).
Descriptions of the scientific agendas of IFML and TRIPODS can be found at ifml.institute and ml.utexas.edu/tripods respectively.
A description of the IFML scientific agenda can be found at ifml.institute.
Fellows will be able to collaborate with numerous researchers and faculty involved in IFML partner institutions: the Machine Learning Lab at UT Austin, the University of Washington, Microsoft Research (Redmond), and Wichita State University. Fellows will play a leading role in organizing seminars, workshops and other research activities. The anticipated term for a fellowship is one or two years - to be decided at the time of appointment, with the possibility of extension based on mutual agreement. In addition to competitive salary and benefits, the fellowship also includes funding for independent travel to workshops, conferences and other universities and research labs.
Simultaneous applications for a joint Simons-UT ML Research Fellowship are possible! Please indicate a simultaneous application in your materials.
Application Instructions
Submission requirements: a CV, research statement, and two reference letters. Applications will be accepted and reviewed on a rolling basis.

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