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Physics Informed Machine Learning Jobs in Cypress, TX

MD Anderson

Postdoc Fellow - Imaging Physics

Houston, TX

$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, mathematical modeling, or medical ...

MD Anderson

Postdoc Fellow - Imaging Physics

Houston, TX

$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, mathematical modeling, or medical ...

MD Anderson

Postdoc Fellow - Imaging Physics

Houston, TX

$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, mathematical modeling, or medical ...

SLB

Nuclear Physicist

Sugar Land, TX · On-site

The broader physics community at HFE has expertise in acoustics, EM, nuclear magnetic resonance ... Experience with machine learning and data science is an advantage but is not required.

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

As of Aug 12, 2026, the average hourly pay for physics informed machine learning in Cypress, TX is $17.28, according to ZipRecruiter salary data. Most workers in this role earn between $10.77 and $21.97 per hour, depending on experience, location, and employer.

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?

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 popular job titles related to Physics Informed Machine Learning jobs in Cypress, TX? For Physics Informed Machine Learning jobs in Cypress, TX, the most frequently searched job titles are:
What job categories do people searching Physics Informed Machine Learning jobs in Cypress, TX look for? The top searched job categories for Physics Informed Machine Learning jobs in Cypress, TX are:
What cities near Cypress, TX are hiring for Physics Informed Machine Learning jobs? Cities near Cypress, TX with the most Physics Informed Machine Learning job openings:
Physics Informed Machine Learning jobs near you
Infographic showing various Physics Informed Machine Learning job openings in Cypress, TX as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $35,945 per year, or $17.3 per hour.

Postdoctoral Fellow - Imaging Physics

MD Anderson Cancer Center

Houston, TX • On-site

$46K - $63K/yr

Full-time

Re-posted 10 days ago

MD Anderson Cancer Center rating

8.4

Company rating: 8.4 out of 10

Based on 170 frontline employees who took The Breakroom Quiz

23rd of 887 rated healthcare providers

Job description

A postdoctoral fellowship position is available in the Department of Imaging Physics in the laboratory of Kristy Brock, Ph.D.
The Morfeus Lab at MD Anderson ( https://www.mdanderson.org/research/departments-labs-institutes/labs/morfeus-laboratory.html) is led by Dr. Kristy Brock. Our research is focused on the development and application of deformable image registration algorithms, artificial intelligence (AI) algorithms, biomechanical modeling, and image guided cancer therapy technologies for strategic research collaborations and clinical translation. Our lab is a highly collaborative research training environment in a state-of-the-art imaging facility and world class resources. Ongoing projects seeking postdocs include:
1) Image-guided liver surgery
2) Image-guided head and neck surgery
3) Cancer risk assessment and outcomes prediction using artificial intelligence
4) Validating in vivo imaging signals using correlative pathology
5) Image-guided focal liver ablation
6) Dose accumulation for personalized adaptive radiation therapy
LEARNING OBJECTIVES
This postdoctoral fellow will engage in computational research projects in image guided cancer therapy, including research, design, and implementation of deformable image registration, biomechanical modeling, and artificial intelligence technologies. The fellow will expand their knowledge and skills in imaging physics, data mining, predictive modeling, clustering, and classification and will develop, train, and apply deep learning in large data environments. The fellow will help to guide research and clinical collaborations and work with collaborators to integrate image guided cancer therapy models and technologies into the clinical workflow. They will collaborate and coordinate with research and clinical stakeholders and communicate findings via abstracts, poster and podium presentations, and publications.
All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations.
ELIGIBILITY REQUIREMENTS
Applicants should have earned a PhD in one of the natural sciences, computer sciences, engineering, or related fields or a medical degree. Experience with machine learning and deep learning techniques, statistical modeling, biomechanical modeling, or image registration and analysis is preferred.
POSITION INFORMATION
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
FACULTY MENTOR
Dr. Kristy Brock

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