Multi-Physics Modeling & Scientific Machine Learning Engineer

• 2+ years of post-doctoral research experience and/or 3+ years of research experience in an industrial or laboratory setting.\n
• Research and development experience in advanced computational methods for multiscale physics applications including fluid mechanics, aerodynamics, reacting flows etc.\n
• Research and development experience in state-of-the-art machine learning algorithms for scientific applications.\n
• Experience in Python, machine learning libraries (such as PyTorch) and knowledge of deployment at scale on high performance compute clusters.\n
• Demonstrated proficiency in high-fidelity multiphysics modeling, cutting edge machine learning methods, and high-performance computing\n
• Outstanding programming skills including expertise with Python, JAX, pyTorch, FORTRAN, C++.\n
• Knowledge of high-fidelity computational fluid mechanics and thermal analysis tools such as Ansys, COMSOL etc.\n
• Ability to lead and /or contribute to the development of research proposals to internal business units and external government agencies such as Department of Energy, Department of Defense etc.\n
• Passion for science and technology and clear evidence of innovation and creativity\n
• Ability to work effectively in a multidisciplinary team\n
• Strong interpersonal skills\n
• Strong analytical skills\n
• Ability to work under pressure and meet deadlines\n
• Excellent written and verbal communication skills\n

At GE Vernova Advanced Research, we invent and develop cross-cutting transformative technologies to enable a clean and sustainable new era of energy. As a Multi-Physics Modeling & Scientific Machine Learning Engineer within the Aerodynamics & Thermosciences team, you will contribute to the development and application of cutting-edge, high-fidelity, multi-physics modeling techniques and methods to solve complex engineering challenges across GE Vernova’s next-generation technology platforms, including and not limited to electrification systems, wind turbines, industrial gas turbines, and nuclear reactors.\n \n You will work in a collaborative, multi-disciplinary environment to revolutionize design and optimization methodologies using physics modeling insights, data-driven physics surrogates of spatio-temporal systems, scientific machine learning at scale and high-fidelity scientific computing on HPC for applications in power generation and electrification technology domains\n \n \nJob Description\n \n \nResponsibilities:\n \n \n

• Independently execute tasks using your technical skills for projects at the confluence of physics, computing and machine learning.\n
• Work as part of a team executing on multi-generation technology programs to enable future design and modeling technology advancements.\n
• Formulate and develop advanced computational methodologies, implement them according to state-of-the-art software practices\n
• Execute high fidelity simulations and perform high fidelity computational analyses.\n
• Use proprietary, opensource and commercial tools as required by the project.\n
• Participate in systematic validation & verification efforts to advance/demonstrate new technologies.\n
• Communicate results effectively with stakeholders and document technical findings.\n, GE Vernova offers a great work environment, professional development, challenging careers, and competitive compensation. GE Vernova is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.\n \n GE Vernova will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable). \n \n \nRelocation Assistance Provided: Yes\n \n For candidates applying to a U.S. based position, the pay range for this position is between $89,300.00 and $148,700.00. The Company pays a geographic differential of 110%, 120% or 130% of salary in certain areas. The specific pay offered may be influenced by a variety of factors, including the candidate’s experience, education, and skill set.\n \n Bonus eligibility: discretionary annual bonus.\n \n This posting is expected to remain open for at least seven days after it was posted on September 03, 2026.\n \n Available benefits include medical, dental, vision, and prescription drug coverage; access to Health Coach from GE Vernova, a 24/7 nurse-based resource; and access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services. Retirement benefits include the GE Vernova Retirement Savings Plan, a tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions, as well as access to Fidelity resources and financial planning consultants. Other benefits include tuition assistance, adoption assistance, paid parental leave, disability benefits, life insurance, 12 paid holidays, and permissive time off.\n \n GE Vernova Inc. or its affiliates (collectively or individually, “GE Vernova”) sponsor certain employee benefit plans or programs GE Vernova reserves the right to terminate, amend, suspend, replace, or modify its benefit plans and programs at any time and for any reason, in its sole discretion. No individual has a vested right to any benefit under a GE Vernova welfare benefit plan or program. This document does not create a contract of employment with any individual.”, “hiringOrganization”: {“@type”: “Organization”, “name”: “GE Vernova”, “logo”: “https://jobs.military.com/attachments/employer/0/961/152/272.svg”}, “jobLocation”: {“address”: {“addressCountry”: “United States”, “streetAddress”: “Not specified”, “@type”: “PostalAddress”, “postalCode”: “12309-1027”, “addressLocality”: “Niskayuna”, “addressRegion”: “New York - NY”}, “@type”: “Place”}, “industry”: “Professional, Scientific, and Technical Services”, “identifier”: {“@type”: “PropertyValue”, “name”: “GE Vernova”, “value”: “227B5349D6E47A6BBFBDF8F56C0BEBF5-a43219”}, “baseSalary”: {“@type”: “MonetaryAmount”, “currency”: “USD”, “value”: {“@type”: “QuantitativeValue”, “value”: “Competitive”, “unitText”