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Physics Informed Machine Learning Jobs in Houston, TX

... management of machine learning and advanced analytics solutions across upstream Oil & Gas ... Knowledge of time-series, forecasting, or physics-informed ML workloads. * Experience with ...

Postdoctoral Fellow - Imaging Physics

Houston, TX · On-site

$46.80K - $63.50K/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 ...

Required : • A Bachelor's in a quantitative field (engineering, mathematics, physics, machine learning, statistics or computer science) are the ideal candidates. • At least 2+ years of industry ...

SLB is seeking applicants for an engineering scientist position in the domain of nuclear physics ... Experience with machine learning and data science is an advantage but is not required.

Postdoc Fellow - Imaging Physics

Houston, TX

$46.80K - $63.50K/yr

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

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How much do physics informed machine learning jobs pay per hour?

As of May 29, 2026, the average hourly pay for physics informed machine learning in Houston, TX is $19.16, according to ZipRecruiter salary data. Most workers in this role earn between $11.92 and $24.33 per hour, depending on experience, location, and employer.

What is a Physics Informed Machine Learning job?

A Physics Informed Machine Learning (PIML) job involves developing AI models that integrate physics-based principles to improve accuracy, interpretability, and generalization. Professionals in this role use machine learning techniques alongside domain knowledge in physics, engineering, or applied sciences to solve complex problems in areas like fluid dynamics, materials science, and climate modeling. Responsibilities often include designing algorithms, implementing simulations, and validating results against experimental or real-world data. Employers typically seek expertise in deep learning, numerical methods, and programming languages like Python.

What are the key skills and qualifications needed to thrive in the Physics Informed Machine Learning position, and why are they important?

To thrive in Physics Informed Machine Learning, you need a solid background in physics, strong mathematical and statistical skills, and experience with machine learning algorithms, typically supported by an advanced degree in a relevant field. Proficiency with programming languages like Python, frameworks such as TensorFlow or PyTorch, and familiarity with numerical simulation tools are commonly required. Effective problem-solving, clear communication, and the ability to collaborate with interdisciplinary teams make a significant impact in this role. These capabilities are essential for developing robust, interpretable machine learning models that leverage physical laws to solve complex, real-world problems.

What are the typical challenges faced by professionals working in Physics Informed Machine Learning roles?

Professionals in Physics Informed Machine Learning often encounter challenges integrating complex physical theories with advanced machine learning models, requiring deep domain knowledge and strong technical skills. Balancing model accuracy with computational efficiency and ensuring that models are both interpretable and generalizable can be demanding. Collaboration with domain experts, data scientists, and engineers is common, as projects often span multiple disciplines. Successfully navigating these challenges provides valuable experience and is highly regarded, often leading to further career advancement in research, engineering, or leadership positions.
What job categories do people searching Physics Informed Machine Learning jobs in Houston, TX look for? The top searched job categories for Physics Informed Machine Learning jobs in Houston, TX are:
What cities near Houston, TX are hiring for Physics Informed Machine Learning jobs? Cities near Houston, TX with the most Physics Informed Machine Learning job openings:
Postdoctoral Fellow - Biostatistics

Postdoctoral Fellow - Biostatistics

MD Anderson

Houston, TX • On-site

$64K - $76K/yr

Other

Medical, Dental, Retirement, PTO

Posted 22 days ago


MD Anderson Cancer Center rating

8.4

Company rating: 8.4 out of 10

Based on 163 frontline employees who took The Breakroom Quiz

32nd of 864 rated healthcare providers


Job description

The Department of Biostatistics at The University of Texas MD Anderson Cancer Center invites applications for a postdoctoral fellowship in bioinformatics, computational biology, and biostatistics. This position is designed for candidates interested in pursuing methodological research in spatial omics data science while also contributing to collaborative translational projects. The fellow will develop novel statistical and computational methods for integrative spatial omics data analysis, derive and implement related computational models, and create software tools for reproducible analysis.

In addition, the fellow will participate in collaborative studies with Dr. Vincent Bernard Pagan's group and other clinical and biological investigators, analyzing spatial omics data generated from clinical trials and related translational research at MD Anderson. The postdoctoral fellow will be supervised by Dr.

Ziyi Li and Dr. Vincent Bernard Pagan and will have the opportunity to contribute to high-impact interdisciplinary projects at the interface of computational methodology, cancer biology, and clinical research. All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations.

LEARNING OBJECTIVES The postdoctoral trainee will develop advanced expertise in statistical and computational methods for integrative spatial omics data analysis, including high-dimensional modeling, feature extraction, multimodal data integration, and spatially informed machine learning. The training will emphasize the development of novel bioinformatics and computational methodologies, including model derivation, algorithm development, software implementation, and reproducible research practices. The trainee will strengthen programming and computational skills for large-scale biological data analysis, gaining extensive experience in R, Python, C++, and related computational platforms.

In addition, the trainee will gain hands-on experience analyzing spatial omics data generated from clinical trials and translational studies, working closely with physicians and biologists to interpret results and generate clinically meaningful insights. Through this process, the trainee will enhance their ability to design, evaluate, and apply innovative statistical and machine learning approaches to complex biomedical problems, while building a strong foundation for independent methodology research, interdisciplinary collaboration, and high-impact scientific publication in biostatistics, bioinformatics, and precision medicine. ELIGIBILITY REQUIREMENTS Applicants must have a recent PhD in bioinformatics/biostatistics/computational biology from a reputed University/Institute or within 0-1 years of graduation.

At least one first author publication in a peer reviewed journal stemming from PhD studies is required. A solid background in spatial omics, single cell data analysis, and computation is required. Some experience with machine learning and AI is desirable.

Please send CV and information on three referees directly to zli16@mdanderson.org. POSITION INFORMATION MD Anderson offers full-time postdoc positions with a salary ranging from $64,000 to $76,000. depending on the number of years of postgraduate experience

The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition Offsite work arrangements are subject to approval and may be modified or revoked at any time based on business needs, performance considerations, or regulatory requirements. This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment

It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html Apply


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