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Temporary Machine Learning Postdoc Jobs in Austin, TX

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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 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:
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.

Postdoctoral Fellow, Department of Computer Science

The University of Texas at Austin

Austin, TX • On-site

$70K/yr

Full-time

Posted 21 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

Job Posting Title:
Postdoctoral Fellow, Department of Computer Science
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Hiring Department:
Department of Computer Science
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Position Open To:
All Applicants
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Weekly Scheduled Hours:
40
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FLSA Status:
Exempt
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Earliest Start Date:
Immediately
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Position Duration:
Expected to Continue Until Dec 31, 2027
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Location:
AUSTIN, TX
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Job Details:
General Notes
Project Affiliation: Federally sponsored research in predictive intelligent networking
Position expected to continue until March 1, 2027.
Must be eligible to work in the United States on a full time basis for any employer.
Purpose
Engineer and productionize components of PIN agents and the supporting training/experiment stack: scalable simulation/emulation harnesses, on-node runtimes, telemetry, and CI/CD. You'll collaborate with researchers implementing multi-agent RL, robust decision-making under uncertainty, and contingency-aware multi-path planning for mobile networks.
What We Offer:
  • A competitive post-doctoral salary and comprehensive benefits.
  • Access to leading-edge technology and research facilities.
  • A collaborative environment that fosters growth and innovation.
  • Involvement in a project with tangible impact on the future of C5ISR systems.

Responsibilities
  • Implement and harden agent/runtime services (Python + C++/Rust), data models, and APIs (gRPC/REST); package for edge compute deployments.
  • Build and maintain simulation→emulation→HIL pipelines (e.g., ns-3/OMNeT++/CORE/EMANE); scenario generators; reproducible experiment harnesses.
  • Integrate training loops (PyTorch/JAX), experiment orchestration, metrics/telemetry (Prometheus/Grafana-class).
  • Own CI/CD, testing, and documentation; contribute to technical reports.
Required Qualifications
  • A Ph.D. in Computer Science, AI, Networking, or a related discipline, earned within the last three years.
  • Solid experience with AI/machine learning methodologies, particularly those applicable to network optimization.
  • Proven ability in programming and familiarity with network simulation tools and environments.
  • A strong propensity for innovative thinking coupled with a disciplined approach to research and collaboration.

Preferred Qualifications
  • Publications or significant contributions to the field of AI, machine learning, or networking.
  • Experience with interdisciplinary research and collaborative projects.
  • Familiarity with military or defense communication systems is a plus.

Salary Range
$70,000+ depending on qualifications
Working Conditions
  • Standard office environment

Required Materials
  • Resume/CV
  • Letter of Interest with research statement
  • 3 reference letters
  • Ph.D. verification

Important for applicants who are NOT current university employees or contingent workers: You will be prompted to submit your resume the first time you apply, then you will be provided an option to upload a new Resume for subsequent applications. Any additional Required Materials (letter of interest, references, etc.) will be uploaded in the Application Questions section; you will be able to multi-select additional files. Before submitting your online job application, ensure that ALL Required Materials have been uploaded. Once your job application has been submitted, you cannot make changes.
Important for Current university employees and contingent workers: As a current university employee or contingent worker, you MUST apply within Workday by searching for Find UT Jobs. If you are a current University employee, log-in to Workday, navigate to your Worker Profile, click the Career link in the left hand navigation menu and then update the sections in your Professional Profile before you apply. This information will be pulled in to your application. The application is one page and you will be prompted to upload your resume. In addition, you must respond to the application questions presented to upload any additional Required Materials (letter of interest, references, etc.) that were noted above.
Employment Eligibility:
Please make sure you meet all the required qualifications and you can perform all of the essential functions with or without a reasonable accommodation.
Retirement Plan Eligibility:
The retirement plan for this position is Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 days in length. This position has the option to elect the Optional Retirement Program (ORP) instead of TRS, subject to the position being 40 hours per week and at least 135 days in length.
Background Checks:
A criminal history background check will be required for finalist(s) under consideration for this position.
Equal Opportunity Employer:
The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.
Pay Transparency:
The University of Texas at Austin will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor's legal duty to furnish information.
Employment Eligibility Verification:
If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form. You will be required to present acceptable and original documents to prove your identity and authorization to work in the United States. Documents need to be presented no later than the third day of employment. Failure to do so will result in loss of employment at the university.
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E-Verify:
The University of Texas at Austin use E-Verify to check the work authorization of all new hires effective May 2015. The university's company ID number for purposes of E-Verify is 854197. For more information about E-Verify, please see the following:
  • E-Verify Poster (English and Spanish) [PDF]
  • Right to Work Poster (English) [PDF]
  • Right to Work Poster (Spanish) [PDF]

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Compliance:
Employees may be required to report violations of law under Title IX and the Jeanne Clery Disclosure of Campus Security Policy and Crime Statistics Act (Clery Act). If this position is identified a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting. Responsible employees under Title IX are defined and outlined in HOP-3031.
The Clery Act requires all prospective employees be notified of the availability of the Annual Security and Fire Safety report. You may access the most recent report here or obtain a copy at University Compliance Services, 1616 Guadalupe Street, UTA 2.206, Austin, Texas 78701.

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