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Senior Quantum Applied Research Scientist, Physics Modeling

$192k - $304.75k

NVIDIA Corporation

At NVIDIA, we're solving the world's most exciting problems with our unique approach to accelerated computing. We're looking for a passionate scientist at the intersection of quantum device physics and advanced physics simulation and modeling. This role will path-find the future of high-fidelity, real-time models for fault-tolerant quantum hardware. At NVIDIA, we want to help accelerate the entire quantum ecosystem. What you’ll be doing Research and develop advanced physics-based models and scalable simulation frameworks. Build physics-informed synthetic data generation pipelines that leverage quantum device models, noise channels, and Hamiltonian characterization to produce high-quality datasets. Develop detailed noise models of quantum hardware that capture device physics, decoherence, and drift behavior, enabling accurate performance prediction and parameter inference without full experimental overhead. Develop GPU-accelerated implementations to ensure the full simulation and modeling pipeline scales. Communicate research findings and collaborate with academic and industry partners to advance the field, while championing rapid innovation, technical depth, and creative problem solving. What we need to see Masters Degree in Physics, Computer Science, Electrical Engineering, Applied Mathematics, or a related field (Ph.D. strongly preferred); or equivalent experience. 8+ years of combined experience and high impact in quantum systems, physics-based modeling, simulation, or related research areas. Hands-on expertise in developing high-fidelity models of physical systems, including numerical simulation and model validation. Strong background in quantum device physics and information science, including noise models, error mechanisms, and fault-tolerant quantum systems across one or more qubit modalities. Broad understanding of quantum control, such as pulse-level hardware interfaces and classical feedback through software abstractions. Experience with scalable computing or accelerated systems for simulation, numerical methods, or modeling workflows. Excellent communication and collaboration skills. Ways to stand out from the crowd Hands-on experience developing simulation frameworks or digital twins of quantum systems and deploying them in calibration or control workflows, with awareness of fidelity, latency, and scalability tradeoffs. Deep expertise in modeling, extracting, and validating noise processes, including system identification, parameter estimation, and uncertainty quantification. Experience with physics-informed or generative approaches to synthetic data generation, including noise simulation, Hamiltonian learning, or data augmentation for scientific workflows. Experience with modeling of more than one qubit modality, including neutral atom qubits. Proficiency with CUDA and NVIDIA GPU programming for accelerating quantum simulation, numerical modeling, or large-scale scientific workloads. Widely considered to be one of the technology world's most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family #LI-Hybrid Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 192,000 USD - 304,750 USD. You will also be eligible for equity and benefits. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. Learn more about NVIDIA. #J-18808-Ljbffr NVIDIA Corporation

Vacancy posted 5 days ago
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