Blacksburg, VA · On-site
$13.25 - $17.50/hr
Physics-Informed Neural Networks (PINNs), operator learning, and neural surrogates; hybrid modeling combining governing equations, simulations, and data; uncertainty-aware learning, interpretability ...
Blacksburg, VA · On-site
$13.25 - $17.50/hr
Physics-Informed Neural Networks (PINNs), operator learning, and neural surrogates; hybrid modeling combining governing equations, simulations, and data; uncertainty-aware learning, interpretability ...
Physics-Informed Neural Networks (PINNs), operator learning, and neural surrogates; hybrid modeling combining governing equations, simulations, and data; uncertainty-aware learning, interpretability ...
Physics-Informed Neural Networks (PINNs), operator learning, and neural surrogates; hybrid modeling combining governing equations, simulations, and data; uncertainty-aware learning, interpretability ...
Advise on the application and integration of machine learning algorithms (e.g., surrogate modeling, physics-informed neural networks, data-driven turbulence modeling) to enhance traditional CFD and ...
Advise on the application and integration of machine learning algorithms (e.g., surrogate modeling, physics-informed neural networks, data-driven turbulence modeling) to enhance traditional CFD and ...
Hampton, VA · On-site +1
$59 - $69/hr
Advise on the application and integration of machine learning algorithms (e.g., surrogate modeling, physics-informed neural networks, data-driven turbulence modeling) to enhance traditional CFD and ...
Hampton, VA · On-site +1
$59 - $69/hr
Advise on the application and integration of machine learning algorithms (e.g., surrogate modeling, physics-informed neural networks, data-driven turbulence modeling) to enhance traditional CFD and ...
Sterling, VA · On-site
Build physics-informed neural networks and digital twin simulations for aerospace systems * Research quantum sensing integration methods for navigation and perception * Document research findings and ...
Sterling, VA · On-site
Build physics-informed neural networks and digital twin simulations for aerospace systems * Research quantum sensing integration methods for navigation and perception * Document research findings and ...
Sterling, VA · On-site
Build physics-informed neural networks and digital twin simulations for aerospace systems * Research quantum sensing integration methods for navigation and perception * Document research findings and ...
Sterling, VA · On-site
Build physics-informed neural networks and digital twin simulations for aerospace systems * Research quantum sensing integration methods for navigation and perception * Document research findings and ...
Understanding of symmetry-aware ML concepts relevant to materials science, including equivariant neural networks, geometric deep learning, and physics-informed or symmetry-preserving model ...
Understanding of symmetry-aware ML concepts relevant to materials science, including equivariant neural networks, geometric deep learning, and physics-informed or symmetry-preserving model ...
... neural networks (BNNs), to solve nuclear engineering problems. * Design hybrid modeling frameworks that combine physics-based and data-driven approaches for applications such as critical heat flux ...
... neural networks (BNNs), to solve nuclear engineering problems. * Design hybrid modeling frameworks that combine physics-based and data-driven approaches for applications such as critical heat flux ...
... neural networks (BNNs), to solve nuclear engineering problems. * Design hybrid modeling frameworks that combine physics-based and data-driven approaches for applications such as critical heat flux ...
... neural networks (BNNs), to solve nuclear engineering problems. * Design hybrid modeling frameworks that combine physics-based and data-driven approaches for applications such as critical heat flux ...
... neural networks (BNNs), to solve nuclear engineering problems. * Design hybrid modeling frameworks that combine physics-based and data-driven approaches for applications such as critical heat flux ...
... neural networks (BNNs), to solve nuclear engineering problems. * Design hybrid modeling frameworks that combine physics-based and data-driven approaches for applications such as critical heat flux ...
... neural networks (BNNs), to solve nuclear engineering problems. * Design hybrid modeling frameworks that combine physics-based and data-driven approaches for applications such as critical heat flux ...
... neural networks (BNNs), to solve nuclear engineering problems. * Design hybrid modeling frameworks that combine physics-based and data-driven approaches for applications such as critical heat flux ...
... neural networks (BNNs), to solve nuclear engineering problems. * Design hybrid modeling frameworks that combine physics-based and data-driven approaches for applications such as critical heat flux ...
... neural networks (BNNs), to solve nuclear engineering problems. * Design hybrid modeling frameworks that combine physics-based and data-driven approaches for applications such as critical heat flux ...
... neural networks (BNNs), to solve nuclear engineering problems. * Design hybrid modeling frameworks that combine physics-based and data-driven approaches for applications such as critical heat flux ...
... neural networks (BNNs), to solve nuclear engineering problems. * Design hybrid modeling frameworks that combine physics-based and data-driven approaches for applications such as critical heat flux ...
Lynchburg, VA · On-site
$86K - $136K/yr
... neural networks (BNNs), to solve nuclear engineering problems. * Design hybrid modeling frameworks that combine physics-based and data-driven approaches for applications such as critical heat flux ...
Lynchburg, VA · On-site
$86K - $136K/yr
... neural networks (BNNs), to solve nuclear engineering problems. * Design hybrid modeling frameworks that combine physics-based and data-driven approaches for applications such as critical heat flux ...
... neural networks (BNNs), to solve nuclear engineering problems. * Design hybrid modeling frameworks that combine physics-based and data-driven approaches for applications such as critical heat flux ...
... neural networks (BNNs), to solve nuclear engineering problems. * Design hybrid modeling frameworks that combine physics-based and data-driven approaches for applications such as critical heat flux ...
... neural networks (BNNs), to solve nuclear engineering problems. * Design hybrid modeling frameworks that combine physics-based and data-driven approaches for applications such as critical heat flux ...
... neural networks (BNNs), to solve nuclear engineering problems. * Design hybrid modeling frameworks that combine physics-based and data-driven approaches for applications such as critical heat flux ...
Roanoke, VA · On-site
$73K - $78K/yr
... physics) or equivalent education, training or experience. Demonstrated experience in machine learning and neural networks. Ability to work independently on multiple deadlines with competing deadlines.
Roanoke, VA · On-site
$73K - $78K/yr
... physics) or equivalent education, training or experience. Demonstrated experience in machine learning and neural networks. Ability to work independently on multiple deadlines with competing deadlines.
... physics) or equivalent education, training or experience. Demonstrated experience in machine learning and neural networks. Ability to work independently on multiple deadlines with competing deadlines.
... physics) or equivalent education, training or experience. Demonstrated experience in machine learning and neural networks. Ability to work independently on multiple deadlines with competing deadlines.
Transition DSP and signal processing-based neural networks to field-ready, real-time software ... BA/BS in electrical engineering, physics, applied mathematics, computer science, or related ...
Transition DSP and signal processing-based neural networks to field-ready, real-time software ... BA/BS in electrical engineering, physics, applied mathematics, computer science, or related ...
... Physics, Statistics, or Applied Mathematics * Proven proficiency in PyTorch, TensorFlow, or other modern machine learning framework * Solid theoretical understanding of deep neural networks as well ...
... Physics, Statistics, or Applied Mathematics * Proven proficiency in PyTorch, TensorFlow, or other modern machine learning framework * Solid theoretical understanding of deep neural networks as well ...
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.
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.
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.
Blacksburg, VA • On-site
$13.25 - $17.50/hr
Full-time
Posted 27 days ago
7.8
Based on 65 frontline employees who took The Breakroom Quiz
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Virginia Tech, guided by its motto "Ut Prosim" (That I May Serve), embraces a hands-on, interdisciplinary approach to educate scholars as leaders and problem-solvers. As a comprehensive land-grant institution, it enriches the quality of life in Virginia and worldwide, fostering an inclusive community focused on knowledge, discovery, and creativity. With over 280 majors, the university serves a diverse student body of more than 36,000 across undergraduate, graduate, and professional programs. Virginia Tech's presence extends throughout Virginia, including campuses in Northern Virginia, Roanoke, Newport News, and Richmond, along with multiple Extension offices and research centers. As a prominent global research institution, it conducts over $500 million in research annually.
Colleges, universities, and professional schools
5,001 - 10,000 Employees
Blacksburg, VA, US
1872
