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Physics Informed Neural Networks Jobs in Texas (NOW HIRING)

Schlumberger

Scientific Computing Intern

Houston, TX

$14.25 - $19/hr

Depending on project progress, the intern may also explore physics-informed neural network (PINN) surrogate modeling approaches for coupled thermal-hydraulic-mechanical-chemical (THMC) behavior ...

Schlumberger

Scientific Computing Intern

Houston, TX · On-site

$14.25 - $19/hr

Depending on project progress, the intern may also explore physics-informed neural network (PINN) surrogate modeling approaches for coupled thermal-hydraulic-mechanical-chemical (THMC) behavior ...

Schlumberger

Scientific Computing Intern

Houston, TX · On-site

$14.25 - $19/hr

Depending on project progress, the intern may also explore physics-informed neural network (PINN) surrogate modeling approaches for coupled thermal-hydraulic-mechanical-chemical (THMC) behavior ...

Adidev Technologies Inc

DATA SCIENTIST

Austin, TX · On-site

S. in Computer Science, Computational Physics, Operations Research, Geospatial Sciences, Remote ... Neural Networks, Naïve Bayes, Bagging & Boosting, Random Forest * Distributed computing tools and ...

Adidev Technologies Inc

DATA SCIENTIST

Austin, TX · Remote

S. in Computer Science, Computational Physics, Operations Research, Geospatial Sciences, Remote ... Neural Networks, Naïve Bayes, Bagging & Boosting, Random Forest * Distributed computing tools and ...

Kemper

Data Scientist

Austin, TX · Hybrid

... neural networks. * Prior experience working with AWS is a plus. * Some experience with natural ... Mathematics, Statistics, CS, Physics, MIS, Economics, Electrical Engineering, etc. * 2+ years ...

Kemper

Data Scientist

Dallas, TX · Hybrid

... neural networks. * Prior experience working with AWS is a plus. * Some experience with natural ... Mathematics, Statistics, CS, Physics, MIS, Economics, Electrical Engineering, etc. * 2+ years ...

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Physics Informed Neural Networks information

What is a physics informed neural network?

A Physics Informed Neural Networks (PINNs) job typically involves developing and applying neural networks that incorporate physical laws as constraints to solve complex scientific and engineering problems. Professionals in this field work on integrating differential equations into deep learning models to improve predictions and reduce the need for large training datasets. These roles are common in fields like fluid dynamics, material science, and climate modeling, where traditional computational methods can be expensive. Individuals in this role often have expertise in machine learning, numerical methods, and domain-specific physics.

What are the key skills and qualifications needed to thrive in physics informed neural networks?

To thrive in Physics Informed Neural Networks (PINNs), you need a strong background in physics, mathematics, and deep learning frameworks, typically evidenced by advanced degrees in physics, applied mathematics, computer science, or engineering. Experience with programming languages such as Python, and familiarity with libraries like TensorFlow or PyTorch, as well as experience in numerical simulation tools, are commonly required. Strong analytical thinking, problem-solving abilities, and effective communication skills help professionals excel in multidisciplinary teams. These qualifications and soft skills are essential for developing accurate, interpretable models that integrate scientific knowledge with machine learning to solve complex real-world problems.

What does a physics informed neural network do?

In a Physics Informed Neural Networks role, your daily tasks will often include designing, building, and testing neural network architectures that incorporate physical laws and constraints. You will frequently collaborate with domain experts, such as physicists or engineers, to integrate scientific knowledge into machine learning models and validate the results with real-world data. Regular responsibilities also involve coding, running experiments, analyzing results, and documenting findings for presentation or publication. This collaborative and research-driven environment helps ensure that models are both accurate and physically consistent, and offers opportunities for interdisciplinary learning and skill advancement.

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Infographic showing various Physics Informed Neural Networks job openings in Texas as of July 2026, with employment types broken down into 93% Full Time, and 7% Part Time. Highlights an 100% In-person job distribution.

Postdoctoral Associate - Specialist

Baylor College of Medicine

Houston, TX

Full-time

Re-posted 16 days ago

Baylor College of Medicine rating

8.0

Company rating: 8.0 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

184th of 616 rated colleges and universities

Job description

Summary

The laboratory of Dr. Lipshutz in the department of Neuroscience at Baylor College of Medicine, Houston, Texas is seeking applications for a Postdoctoral Associate - Specialist position. Postdoctoral Associate - Specialist will develop, model, and test normative theories of adaptive phenomenon in biological neural networks. They will collaborate with theorists and experimentalists at Baylor College of Medicine, Rice University, and, more broadly, the Texas Medical Center.

Job Duties
  • Develops normative models of adaptive computations in biological neural networks and test these models on neural data collected by experimental collaborators.
  • Plans, directs, and conducts an advanced research program.
  • Reads the relevant background on experimental and theoretical literature.
  • Presents findings at conferences and publishes results in research journals.
  • Performs other job-related duties as assigned.
Minimum Qualifications
  • Ph.D. in Chemistry, Computational Sciences, Computational Biology, Structural Biology, Computer Science, Bioinformatics, Statistics, or related disciplines. May also include Ph.D. in Biology or Biomedical Sciences in combination with an M.S. or extensive multidisciplinary experience in one of the above quantitative fields.
Preferred Qualifications
  • Ph.D. in Computational Neuroscience, Physics, Electrical Engineering, Statistics, Mathematics, or related disciplines.
  • Strong foundation developing theoretical models is preferred.

Work Authorization Requirement:

This position is not eligible for visa sponsorship. Candidates must be legally authorized to work in the United States at the time of application and throughout the duration of employment. 

Baylor College of Medicine is an Equal Opportunity/Affirmative Action/Equal Access Employer.

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