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Physics Informed Neural Networks Jobs in Dallas, TX

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 ...

Socket.dev

Senior Data Scientist

Frisco, TX · On-site

$128 - $180/hr

Master's degree in Statistics, Econometrics, Mathematics, Operations Research, Physics, Computer ... neural networks, deep learning and its various applications. Continuously following the advancement ...

New

Micron

Staff HBM Design Architect

Richardson, TX

$146K - $297K/yr

... neural networks. Our work combines deep technical expertise with a strong culture of integrity ... Strong knowledge of CMOS circuit design and semiconductor device physics * Experience with ...

Micron

Staff HBM Design Architect

Richardson, TX · On-site

$146K - $297K/yr

... neural networks. Our work combines deep technical expertise with a strong culture of integrity ... Strong knowledge of CMOS circuit design and semiconductor device physics * Experience with ...

USAA

Senior Decision Science Analyst - P&C Analytics

Plano, TX · On-site

$84K - $111K/yr

Remains informed on current data and analytics trends, (Ex: Cloud, Data Mining, Python, Neural Networks, Sensor data, IoT, Streaming/NRT data) * Identifies opportunities to continue to learn in the ...

AT&T

Senior Data Scientist

Dallas, TX · On-site

Generative Models-Understanding of GANs (Generative Adversarial Networks), VAEs (Variational ... or Physics. 3+ years of related experience. Certification is required in some areas. Our Senior ...

All JobsPhysics Informed Neural Networks JobsPhysics Informed Neural Networks Jobs in Dallas, TX

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$5

$19

$25

How much do physics informed neural networks jobs pay per hour?

As of Aug 8, 2026, the average hourly pay for physics informed neural networks in Dallas, TX is $19.85, according to ZipRecruiter salary data. Most workers in this role earn between $12.36 and $25.19 per hour, depending on experience, location, and employer.

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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Physics Informed Neural Networks jobs near you

POSTDOCTORAL RESEARCHER - O'Donnell Brain Institute - Cone Lab [Req#: 961927, Position#: 132376]

UT Southwestern Medical Center

Dallas, TX • On-site

Full-time

Posted 11 days ago

UT Southwestern rating

7.9

Company rating: 7.9 out of 10

Based on 152 frontline employees who took The Breakroom Quiz

108th of 887 rated healthcare providers

Job description

Description
The Cone Lab in the O'Donnell Brain Institute at UT Southwestern Medical Center is seeking a postdoctoral research scientist to help develop biophysically grounded models of synaptic plasticity and circuit function in the hippocampus. The position is in the Program in Memory and Longevity, under the direction of Dr. Ian Cone.
The postdoc will investigate the mechanisms and computational consequences of behavioral timescale synaptic plasticity (BTSP) in hippocampal circuits. This includes building models of plasticity at the single-neuron level and studying how BTSP shapes learning and representation in hippocampal networks, in close collaboration with experimental partners. The postdoc will also contribute to the lab's broader program on synaptic plasticity and hippocampal computation.
Responsibilities:
  • Develop biophysically grounded models of synaptic plasticity, with a focus on BTSP and dendritic mechanisms
  • Connect cellular-level models to network-scale computation and dynamics
  • Collaborate with experimental partners to constrain and test models against imaging and electrophysiology data
  • Contribute to the lab's broader research program spanning synaptic plasticity, credit assignment, and hippocampal computation
  • Publish research in relevant journals
  • Present research at relevant conferences and seminars
  • Mentor and support junior scientists in the lab

Qualifications
  • Ph.D. in computational neuroscience, biophysics, physics, applied mathematics, biomedical engineering, or a related quantitative field.
  • Strong background in computational modeling of biologically plausible neural networks and synaptic plasticity
  • Experience with simulation platforms such as NEURON, Jaxley, Brian2, or equivalent.
  • Proficiency in Python and/or MATLAB
  • Desire to work at the interface of theory and experiment.
  • Record of peer-reviewed publications.

Application Instructions
Please submit CV and a brief summary of research accomplishments and research goals.
Equal Employment Opportunity Statement
UT Southwestern Medical Center is committed to an educational and working environment that provides equal opportunity to all members of the University community. As an equal opportunity employer, UT Southwestern prohibits unlawful discrimination, including discrimination on the basis of race, color, religion, national origin, sex, sexual orientation, gender identity, gender expression, age, disability, genetic information, citizenship status, or veteran status.
This position is security-sensitive and subject to Texas Education Code 51.215, which authorizes UT Southwestern to obtain criminal history record information.
Appointment rank will be commensurate with academic accomplishment and experience. Consideration may be given to applicants seeking less than a full-time schedule.
To learn more about the benefits UT Southwestern offers, visit https://www.utsouthwestern.edu/employees/hr-resources/

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