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Physics Informed Machine Learning Jobs in Texas (NOW HIRING)

... informed machine learning. The training will emphasize the development of novel biostatistical and computational methodologies, including model derivation, algorithm design, software implementation ...

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

Data Engineer (Starlink Growth)

Bastrop, TX · On-site

$113K - $136K/yr

You will bring in the latest and greatest machine learning and statistical technologies to turn ... Bachelor's degree in computer science, physics, mathematics, statistics, or a STEM discipline * 1+ ...

Sr. Data Engineer (Starlink Grwoth)

Bastrop, TX · On-site

$113K - $136K/yr

You will bring in the latest and greatest machine learning and statistical technologies to turn ... Bachelor's degree in computer science, physics, mathematics, statistics, or a STEM discipline * 5+ ...

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Physics Informed Machine Learning information

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 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 cities in Texas are hiring for Physics Informed Machine Learning jobs? Cities in Texas with the most Physics Informed Machine Learning job openings:
Infographic showing various Physics Informed Machine Learning job openings in Texas as of June 2026, with employment types broken down into 1% Locum Tenens, 82% Full Time, 11% Part Time, 1% Temporary, 3% Contract, and 2% Nights. Highlights an 72% Physical, 3% Hybrid, and 25% Remote job distribution.
Postdoctoral Fellow - Biostatistics

Postdoctoral Fellow - Biostatistics

MD Anderson Cancer Center

Houston, TX • On-site

$64K - $76K/yr

Full-time

Medical, Dental, Retirement, PTO

Posted 12 days ago


MD Anderson Cancer Center rating

8.4

Company rating: 8.4 out of 10

Based on 164 frontline employees who took The Breakroom Quiz

34th of 873 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 Biostatistics. This position is intended for candidates interested in developing methodological research in data science and artificial intelligence, with applications to biostatistics and cancer research. The postdoctoral fellow will develop novel statistical and computational methods for the integrative analysis of multi-source biomedical data and will create software tools to support reproducible research. The fellow will also contribute to collaborative translational studies with clinical and biological investigators, including the analysis of omics, survival, clinical trial, and other clinical data generated through cancer research at UT MD Anderson. The fellow will be jointly supervised by Dr. Xuelin Huang and Dr. Ziyi Li and will have the opportunity to contribute to high-impact interdisciplinary projects at the interface of statistical methodology, artificial intelligence, 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 clinical and survival data analysis, including high-dimensional modeling, feature extraction, multimodal data integration, and spatially informed machine learning. The training will emphasize the development of novel biostatistical and computational methodologies, including model derivation, algorithm design, software implementation, and reproducible research practices. The trainee will further strengthen programming and computational skills for large-scale biomedical data analysis, with extensive experience in R, Python, C++, and related computational platforms. In addition, the trainee will gain hands-on experience analyzing clinical, survival, and omics data from clinical trials and translational studies, working closely with physicians and biologists to interpret findings and generate clinically meaningful insights. Through this training, the fellow will be well prepared to design, evaluate, and apply innovative statistical and machine learning approaches to complex biomedical problems, while building a strong foundation for independent methodological research, interdisciplinary collaboration, and high-impact scientific publication in biostatistics, bioinformatics, and precision medicine.
ELIGIBILITY REQUIREMENTS
Applicants must have a recent PhD in biostatistics/computational science 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.
ADDITIONAL APPLICATION INFORMATION
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

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