Lead the full model development lifecycle for MATLAB and Simulink simulation models supporting wind turbine control systems.
Responsibilities
Lead the full model development lifecycle, from requirements clarification and functional specification through implementation, review, validation, documentation, and release.
Develop and maintain modular MATLAB and Simulink models for wind turbine sensors, actuators, and control logic.
Translate supplier datasheets, interface definitions, and test data into model equations, parameters, operating limits, and acceptance criteria.
Build models that reflect real system behavior, including delays, saturation, noise, drift, filtering, rate limits, hysteresis, diagnostics, and fault responses.
Define and automate requirements-based verification and validation activities, including regression testing for normal operation, transients, limits, and fault conditions.
Requirements
Bachelor’s degree in Control Engineering, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, Applied Mathematics, or a related field, or equivalent professional experience.
The internship focuses on the numerical simulation of stamping processes to improve process robustness, specifically focusing on the feasibility of vehicle body parts.
Responsibilities
Statistical analysis of process parameters affecting the feasibility of complex parts using numerical simulation.
Study of the influence of the friction coefficient on deep parts using an advanced numerical model associated with numerical simulation.
Correlation between numerical simulation results and physical parts to improve process robustness.
Communicate with various company entities (laboratories, stamping departments, factories) to collect necessary information.
Internship in reservoir engineering providing hands‑on experience in reservoir analysis, simulation, and economic evaluation within Oxy’s core businesses.
Locations (4)
Houston, Texas, United States
The Woodlands, Texas, United States
Colorado
Texas, United States
Responsibilities
Apply reservoir engineering concepts to understand and optimize production and recovery
Utilize fundamental reservoir engineering to quantify performance
Quantify reservoir analysis fundamentals such as pressure transient analysis, decline curve analysis, material balance, and numerical simulation
PhD Scholarships in Quantum Physics: Quantum Opto-Acoustics, Optomechanics, Microscopy and Computing - DTU Physics
A scientist role at the intersection of quantum device physics and advanced physics simulation and modeling, path-finding the future of high-fidelity, real-time models for fault-tolerant quantum hardware.
Compensation
The expected base salary range for this position is 192,000 USD – 304,750 USD.
Responsibilities
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.
A technically proficient role bridging customers, product management, and development teams using CAE, design verification, and AI expertise.
Compensation
The pay range for this position is $109,800 - $ 197,700 annually.
Responsibilities
Collaborate closely with Product Management and R&D/Development teams to translate complex customer requirements and workflows into detailed, actionable feature plans and technical specifications.
Drive feature planning, tracking, and execution throughout the product and release management lifecycle, ensuring alignment with enterprise engineering standards.
Act as the technical liaison between end-users, support, product managers, and software developers to ensure seamless delivery of enterprise-grade engineering tools.
Design, validate, and optimize CAE and engineering AI methods to enhance design verification and validation processes.
Provide expert guidance on the integration of AI-driven engineering applications within High-Performance Computing (HPC) environments.
Provide reservoir engineering support focused on individual basins and asset team needs within Continental’s Reservoir Solutions Team.
Responsibilities
Use numerical simulation, rate and pressure transient analysis to provide reservoir insights and recommendations on development strategy.
Design, implement and analyze reservoir surveillance programs.
Perform computer simulations of oil and gas reservoirs to aid in reservoir management including analytical, black oil and compositional models.
Analyze and interpret short- and long-term production performance from unconventional reservoirs to aid well completion, workover operations and relate well development strategy to well performance.
Provide direction and expertise on data collection and laboratory testing activities to meet specific reservoir characterization needs.
Requirements
Bachelor of Science in Petroleum, Mechanical, or Chemical Engineering or related engineering discipline from an accredited college or university.
Develop next-generation AI models for semiconductor engineering by combining scientific domain knowledge with modern machine learning. Build physics-aware surrogate models, foundation models, and optimization workflows that accelerate simulation and engineering design.
Compensation
Salary: $161,000.00 - $221,000.00
Responsibilities
Develop Scientific AI models that bridge scientific computing, physics-based simulation and machine learning for physics and chemistry-based engineering problems.
Design and train surrogate models, operator-learning models, and foundation models for scientific simulations.
Collaborate with domain experts to formulate AI solutions for complex engineering challenges.
Develop scalable data generation, training, validation and deployment workflows for Scientific AI models.
Publish technical innovations and drive adoption of Scientific AI across engineering applications.
CS27 Stage - BAC+5 – Chargé d'étude pour la robustesse des process d’emboutissage à l’aide de la simulation numérique - H/FNewRenault Group · Guyancourt, France
PhD Scholarships in Quantum Physics: Quantum Opto-Acoustics, Optomechanics, Microscopy and Computing - DTU PhysicsDTU Career Site · Kgs. Lyngby, Denmark