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Temporary Machine Learning Postdoc Jobs in Pearland, TX

MD Anderson

Postdoctoral Fellow - Imaging Physics

Houston, TX · On-site

$46K - $63K/yr

A postdoctoral fellowship position is available in the Department of Imaging Physics in the ... Experience with machine learning and deep learning techniques, statistical modeling, biomechanical ...

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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 cities near Pearland, TX are hiring for Temporary Machine Learning Postdoc jobs?

Cities near Pearland, TX with the most Temporary Machine Learning Postdoc job openings:

Temporary Machine Learning Postdoc jobs near you

Postdoctoral Associate - Earth Environmental Planetary Sciences

Rice University

Houston, TX • On-site

$65K/yr

Full-time

Posted yesterday

New

Rice University rating

8.2

Company rating: 8.2 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

147th of 620 rated colleges and universities

Job description

Position Summary

Dr. Ian McBrearty's lab in the Department of Earth, Environmental and Planetary Sciences is looking to hire a Postdoctoral Research Associate in the field of Machine Learning & Geophysics.

The McBrearty Lab sits at the intersection of machine learning and physics, focusing on developing data-driven techniques for earthquake monitoring, processing data from large seismic networks, and developing neural-surrogate emulations of PDEs governing tectonic and volcanic processes.

The ideal candidate will hold a Ph.D. in a quantitative field with strong Python and deep learning skills to build data-driven tools for earthquake detection and geophysical forecasting. They will be responsible for developing graph neural networks (GNNs), advancing PDE emulation methods, publishing high-impact research, and utilizing high-performance computing resources. Review of applications begins September 1, 2026, and will continue until the position is filled. Informal inquiries can be sent to Dr. Ian McBrearty at im76@rice.edu.

Workplace Requirements:

On campus position: This position is exclusively on-site, necessitating all duties to be performed in-person in Houston, Texas. Per Rice policy 440, work arrangements may be subject to change.

*Exempt (salaried) positions under FLSA are not eligible for overtime.

This position is funded by a grant, soft and/or restricted funds. Continued employment is contingent on the renewal of funding.

Proposed Salary: $65,000

Essential Functions

  • Develops and deploys machine learning models (specifically graph neural networks) to process large, spatially irregular seismic datasets and advance neural-surrogate emulations of PDEs governing geophysical processes

  • Documents, analyzes, and maintains research data

  • Publishes and presents research findings

  • Supports project management and collaboration across institutions or disciplines

  • Performs all other duties as assigned

Required Qualifications and Skills

  • Ph.D. in Geophysics, Computer Science, Data Science, Applied Mathematics, or a related quantitative field

  • Strong Python programming skills and practical experience with deep learning frameworks (e.g., PyTorch, TensorFlow) for scientific data analysis

  • Excellent verbal and written communication skills, as well as oral presentation skills

  • Organization and time management skills

  • Knowledge of modern research methods, data collection, and analyses

  • Ability to write scholarly papers based on ongoing research in order to submit them to journals for publication

  • Able to work in a collaborative environment

  • Able to work independently and professionally with minimal supervision and direction

Preferred Qualifications

  • Experience with Graph Neural Networks (GNNs) or physics-informed machine learning (PINNs).

  • Background in seismological software packages (e.g., ObsPy) and large-scale, high-performance computing (HPC) data processing.

Rice University HR | Benefits: https://knowledgecafe.rice.edu/benefits 
 

Rice Mission and Values: Mission and Values | Rice University 

Rice University is committed to ensuring Equal Employment Opportunity and welcoming the fullness of diversity into our candidate pools. Rice considers qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national or ethnic origin, genetic information, disability, or protected veteran status. Rice also provides reasonable accommodations to qualified persons with disabilities. If an applicant requires a reasonable accommodation for any part of the application or hiring process, please get in touch with Rice University's Human Resources Office via email at facstaffada@rice.edu for support.

If you have any additional questions, please email us at jobs@rice.edu . Thank you for your interest in employment with Rice University

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