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Machine Learning Postdoc Jobs in Houston, TX (NOW HIRING)

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Machine Learning Postdoc information

What is a machine learning postdoc?

A Machine Learning Postdoc is a research-focused position typically held after earning a Ph.D. in a related field. It involves conducting advanced research in machine learning, developing new algorithms, and publishing in top-tier conferences and journals. Postdocs often collaborate with faculty, industry partners, and other researchers to advance the state of the art in AI. The role may include mentoring students and contributing to grant proposals. It serves as a bridge between doctoral studies and a long-term academic or industry research career.

What are the typical responsibilities and collaborative aspects of a machine learning postdoc?

A Machine Learning Postdoc typically conducts original research, develops and tests new algorithms, and contributes to academic publications or patent applications. Daily tasks often involve data analysis, model building, and experimentation using advanced computational tools. Collaboration is key in this role, as postdocs frequently work alongside faculty, graduate students, and external industry partners to advance research objectives. Additionally, they may mentor junior researchers or students, present at conferences, and participate in grant writing or project planning. This mix of independent research and team collaboration fosters both professional growth and impactful scientific advancements.

What are the key skills and qualifications needed to thrive in a machine learning postdoc position?

To thrive as a Machine Learning Postdoc, you need a deep understanding of machine learning algorithms, statistical modeling, and research methodology, typically supported by a completed PhD in a related field. Proficiency with programming languages like Python or R, experience with ML libraries (e.g., TensorFlow or PyTorch), and familiarity with large-scale datasets and cloud computing platforms are important. Strong analytical thinking, effective communication, and the ability to collaborate across multidisciplinary teams are standout soft skills in this position. These qualifications ensure innovative research contributions, successful project execution, and effective dissemination of findings in both academic and applied settings.

What are popular job titles related to Machine Learning Postdoc jobs in Houston, TX?

For Machine Learning Postdoc jobs in Houston, TX, the most frequently searched job titles are:

What job categories do people searching Machine Learning Postdoc jobs in Houston, TX look for?

The top searched job categories for Machine Learning Postdoc jobs in Houston, TX are:

What cities near Houston, TX are hiring for Machine Learning Postdoc jobs?

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

Machine Learning Postdoc jobs near you
Infographic showing various Machine Learning Postdoc job openings in Houston, TX as of August 2026, with employment types broken down into 94% Full Time, and 6% Contract. Highlights an 78% In-person, 7% Hybrid, and 15% Remote job distribution.

Postdoctoral Associate - Earth Environmental Planetary Sciences

Rice University

Houston, TX

$65K/yr

Full-time

Posted 17 days ago

Rice University rating

8.2

Company rating: 8.2 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

151st of 631 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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