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Physics Informed Machine Learning Jobs in Texas (NOW HIRING)

Schlumberger

FWI & AI Scientist

Houston, TX · On-site

Expertise in physics-informed machine learning, learned solvers, or neural surrogate models. * Proven success applying deep learning to physics-based modeling and simulation * Experience deploying ML ...

ExxonMobil

Computational Scientist

Spring, TX · On-site

Experience in surrogate modeling approaches (e.g., deep learning, machine learning, physics-informed machine learning, reduced-order modeling, multi-fidelity methods, etc.) to reduce computational ...

ExxonMobil

Computational Scientist

Spring, TX · On-site

Experience in surrogate modeling approaches (e.g., deep learning, machine learning, physics-informed machine learning, reduced-order modeling, multi-fidelity methods, etc.) to reduce computational ...

ExxonMobil

Computational Scientist

Spring, TX · On-site

Experience in surrogate modeling approaches (e.g., deep learning, machine learning, physics-informed machine learning, reduced-order modeling, multi-fidelity methods, etc.) to reduce computational ...

Cymertek

Machine Learning Engineer

San Antonio, TX · On-site

Machine Learning Engineer LOCATIONSan Antonio, TX 78208 CLEARANCETS/SCI Full Poly (Please note this ... Physics, ect.ALTERNATE EXPERIENCEGeneral comment on degrees: Most contracts allow additional ...

Shipwell

Machine Learning Engineer

Austin, TX · On-site +1

About the Role As a Machine Learning Engineer at Shipwell, you'll play a pivotal role in building ... Bachelor's Degree in a quantitative field such as Physics, Engineering, Computer Science, or ...

Photon

Machine Learning Engineer - NJ

Dallas, TX · On-site

Machine Learning Engineer We are seeking a Machine Learning Engineer to design and develop robust ... Undergraduate or Graduate degree in Computer Science, Mathematics, Physics, or related fields. A ...

Oxy

AI Machine Learning Engineer

Houston, TX · On-site

Bachelor's degree in computer science, mathematics, physics, engineering or related field required ... machine learning methods and algorithms * Strong Python or R programming skills required

Oxy

AI Machine Learning Engineer

Houston, TX · On-site

Bachelor's degree in computer science, mathematics, physics, engineering or related field required ... machine learning methods and algorithms * Strong Python or R programming skills required

Oxy

AI Machine Learning Engineer

Houston, TX

Bachelor's degree in computer science, mathematics, physics, engineering or related field required ... machine learning methods and algorithms * Strong Python or R programming skills required

NobleAI

Senior Applied AI/ML Scientist

Houston, TX · On-site +1

By combining physics and chemistry expertise with advanced machine learning, our platform improves ... Familiarity with physics-informed ML, geological/reservoir modeling, and/or production forecasting ...

Cymertek

Machine Learning Engineer

San Antonio, TX · On-site

Machine Learning Engineer LOCATION San Antonio, TX 78208 CLEARANCE TS/SCI Full Poly (Please note ... Physics, ect. ALTERNATE EXPERIENCE General comment on degrees: Most contracts allow additional ...

Photon

Machine Learning Engineer - NJ

Addison, TX · On-site

$54 - $71.50/hr

We are seeking a Machine Learning Engineer to design and develop robust analytics models using ... Undergraduate or Graduate degree in Computer Science, Mathematics, Physics, or related fields. A ...

Photon

Machine Learning Engineer - NJ

Addison, TX · On-site

$54 - $71.50/hr

We are seeking a Machine Learning Engineer to design and develop robust analytics models using ... Undergraduate or Graduate degree in Computer Science, Mathematics, Physics, or related fields. A ...

Photon

Machine Learning Engineer - NJ

Addison, TX

$54 - $71.50/hr

We are seeking a Machine Learning Engineer to design and develop robust analytics models using ... Undergraduate or Graduate degree in Computer Science, Mathematics, Physics, or related fields. A ...

Vizient

AVP, Machine Learning & Modeling

Irving, TX · On-site

$156.50K - $290.10K/yr

... drive data-informed decision-making across the enterprise. Oversee teams of data scientists ... Strategic Leadership and Vision Provide strategic direction for the organization's machine learning ...

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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.
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Physics Informed Machine Learning jobs near you
FWI & AI Scientist

FWI & AI Scientist

Schlumberger

Houston, TX • On-site

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Schlumberger rating

7.4

Company rating: 7.4 out of 10

Based on 76 frontline employees who took The Breakroom Quiz

238th of 415 rated machine equipment manufacturers

Job description

Job Title: FWI & AI Scientist

  • Lead the development of the FWI Foundation Model Design and scale a generalizable hybrid physics-AI foundation model on real seismic datasets.Propose advanced techniques including AI-assisted velocity model building, learned regularization and preconditioning, cycle-skipping mitigation, and accelerated forward modeling/gradient computation.Scale implementations for GPU clusters, HPC systems, and large-scale 3D datasets.

  • Integrate AI into FWI and imaging pipelines Seamlessly embed AI components into existing Full Waveform Inversion and seismic imaging workflows.Promote best practices in scientific software development and ML lifecycle management.

  • Validate, deploy, and drive business impact Validate solutions on field data in complex geological settings and establish clear performance, robustness, and risk metrics.Collaborate with stakeholders to ensure geological accuracy and commercial relevance.Mentor the team on FWI fundamentals while driving adoption of modern AI/ML techniques.

Required Qualifications

  • PhD/MS in Geophysics, Applied Math, Physics, Computer Science, or equivalent experience

  • Deep expertise in Full Waveform Inversion, seismic wave propagation, and inverse problems

  • Prior hands-on industry experience in subsurface imaging is a plus

  • Track record of developing and delivering production-grade scientific software in HPC or cloud environments, including data pipeline and imaging algorithm

Preferred Qualifications

  • Expertise in physics-informed machine learning, learned solvers, or neural surrogate models.

  • Proven success applying deep learning to physics-based modeling and simulation

  • Experience deploying ML models at scale (lifecycle management, monitoring, reproducibility)

  • Track record of high-impact publications, patents, or industrial innovations

  • Strong Python programming skills with hands-on experience in PyTorch or JAX

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