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Physics Informed Machine Learning Jobs in Texas (NOW HIRING)

MD Anderson

Postdoctoral Fellow - Biostatistics

Houston, TX · On-site

$64K - $76K/yr

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

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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 are popular job titles related to Physics Informed Machine Learning jobs in Texas? For Physics Informed Machine Learning jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Physics Informed Machine Learning jobs in Texas look for? The top searched job categories for Physics Informed Machine Learning jobs in Texas are:
What cities in Texas are hiring for Physics Informed Machine Learning jobs? Cities in Texas with the most Physics Informed Machine Learning job openings:
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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.
Data Scientist, Upstream Development

Data Scientist, Upstream Development

ExxonMobil

Spring, TX • On-site

Part-time

Medical, Dental, Vision, Life, Retirement

Posted 4 days ago

ExxonMobil rating

6.1

Company rating: 6.1 out of 10

Based on 221 frontline employees who took The Breakroom Quiz

55th of 74 rated oil and gas companies

Job description

About us
At ExxonMobil, our vision is to lead in energy innovations that advance modern living and a net-zero future. As one of the world's largest publicly traded energy and chemical companies, we are powered by a unique and diverse workforce fueled by the pride in what we do and what we stand for.
The success of our Upstream, Product Solutions and Low Carbon Solutions businesses is the result of the talent, curiosity and drive of our people. They bring solutions every day to optimize our strategy in energy, chemicals, lubricants and lower-emissions technologies.
We invite you to bring your ideas to ExxonMobil to help create sustainable solutions that improve quality of life and meet society's evolving needs. Learn more about our What and our Why and how we canwork together.
What you will do
Lead the design, development, and deployment of advanced AI/ML solutions for upstream oil & gas subsurface & well operations such as physics-informed Machine Learning (ML) and production optimization. You will work end-to-end from problem framing and experimentation through production and sustainment partnering with engineers and business stakeholders to deliver scalable, reliable, and impactful solutions.
What role will you play in the team
  • Lead end-to-end delivery of AI/ML solutions: scoping, modeling, evaluation, deployment, and monitoring.
  • Collaborate with cross-functional teams to translate business problems into mathematical frameworks.
  • Build solutions in one or more domains listed above, applying best practices for data quality, explainability, and governance.
  • Develop GenAI applications (chatbots, copilots, multi-agent workflows) and/or time series, optimization, or commercial analytics models.
  • Ensure production readiness through MLOps practices (CI/CD, MLflow, monitoring, cost optimization).
  • Mentor peers and contribute to internal AI capability building.

About you
Desired Skills
  • 5+ years of direct experience delivering production AI/ML solutions.
  • Experience applying data science to upstream oil & gas workflows, including exploration, drilling and reservoir management; analyzing well logs, seismic data, and production metrics to support operational and strategic decisions
  • Strong foundations in statistics, probability, and algorithm design.
  • Proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn); experience with Databricks/Spark.
  • Familiarity with GenAI frameworks (LangChain, Promptflow) and/or optimization libraries.
  • Experience with MLOps, model governance, and explainable AI techniques (e.g., SHAP, LIME).
  • Excellent communication and collaboration skills.

Preferred Knowledge/Skills
  • Cloud platforms (Azure ML, Azure OpenAI, Databricks).
  • Knowledge graphs, hybrid search/RAG, and semantic technologies.
  • Agile development and software engineering best practices.

Educational Background Recommended
  • Master's or PhD in Data Science, Computer Science, Engineering, Applied Math, or related field.

Your benefits
An ExxonMobil career is one designed to last. Our commitment to you runs deep: our employees grow personally and professionally, with benefits built on our core categories of health, security, finance, and life.
We offer you:
  • Pension Plan: Enrollment is automatic and at no cost to you. The basic benefit is a monthly annuity to be paid to you in retirement for the rest of your life.
  • Savings Plan: You can contribute between 6% and 20% of your pay and are encouraged to enroll right away. If you contribute at least 6% to your savings plan, the Company will contribute a 7% match.
  • Workplace Flexibility: We have several programs such as "Flex your Day", providing ad-hoc flexibility around when and where you work, as well as longer-term programs such as leaves of absence and part-time work.
  • Comprehensive medical, dental, and vision plans.
  • Culture of Health: Programs and resources to support your wellbeing.
  • Employee Health Advisory Program: Provides confidential professional counseling for you and your family, including tools and resources promoting mental health and resiliency at no additional cost to you.
  • Disability Plan: Income replacement for when you cannot work due to illness or injury occurring on or off the job. Enrollment is automatic and at no cost to you.

More information on our Company's benefits can be found at www.exxonmobilfamily.com.
Please note benefits may be changed from time to time without notice, subject to applicable law.
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Nothing herein is intended to override the corporate separateness of local entities. Working relationships discussed herein do not necessarily represent a reporting connection, but may reflect a functional guidance, stewardship, or service relationship.
Exxon Mobil Corporation has numerous affiliates, many with names that include ExxonMobil, Exxon, Esso and Mobil. For convenience and simplicity, those terms and terms like corporation, company, our, we and its are sometimes used as abbreviated references to specific affiliates or affiliate groups. Abbreviated references describing global or regional operational organizations and global or regional business lines are also sometimes used for convenience and simplicity. Similarly, ExxonMobil has business relationships with thousands of customers, suppliers, governments, and others. For convenience and simplicity, words like venture, joint venture, partnership, co-venturer, and partner are used to indicate business relationships involving common activities and interests, and those words may not indicate precise legal relationships.

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