New College Grad - Computational Physics & Agentic AI Engineer, Doctorate Degree (Spring 2027)

Who We Are Applied Materials is the global leader in materials science and engineering solutions that are at the foundation of virtually every new semiconductor chip and advanced display in the world. The equipment that we create and service is essential to advancing AI and accelerating the commercialization of next-generation semiconductor chips. Join us and push the boundaries of materials science and engineering in a company at the foundation of the electronics industry. The work we do together advances the world’s technology. What We Offer Salary: $138,000.00 - $190,000.00 Location: Santa Clara,CA You’ll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible—while learning every day in a supportive leading global company. Visit our Careers website to learn more. At Applied Materials, we care about the health and wellbeing of our employees. We’re committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits. About the Role As a Computational Physics, Plasma Modeling & Agentic AI Engineer, you will join a computational modeling team that develops and applies advanced physics-based simulation tools and digital twins for semiconductor process chambers and plasma processing applications. Your work will help improve the understanding and prediction of thermal and plasma-driven processes in semiconductor equipment, including deposition, etching, plasma-enhanced processing, and plasma-surface interactions, while also making these modeling capabilities more accessible to engineers across the organization through modern AI-driven software and workflow tools. This role is ideal for an early-career PhD-level scientist or engineer who enjoys combining rigorous first-principles modeling, large-scale simulation, and software development. You will work at the intersection of computational physics, high-performance computing, and AI/ML to accelerate technology and product development in semiconductor equipment. Applications will be reviewed on a rolling basis. Please apply by November 20, 2026. This position may close earlier based on application volume or candidate selection. Your work will span three core areas: Modeling Capability – develop and apply advanced multi-scale and multi-fidelity models for thermal and plasma-based semiconductor processes, linking high-fidelity solvers with reduced-order and surrogate approaches, with quantified accuracy and clearly defined limits of validity. Modeling Capability – develop and apply advanced multi-scale and multi-fidelity models for thermal and plasma-based semiconductor processes, linking high-fidelity solvers with reduced-order and surrogate approaches, with quantified accuracy and clearly defined limits of validity. Verification & Validation – establish rigorous methods to verify computational models and AI-enabled workflows against reference solutions and validate their predictions against experimental data, with the infrastructure needed to maintain reliability as the capabilities evolve. Key Responsibilities Apply computational modeling to real semiconductor process and equipment development challenges, including thermal, plasma, deposition, etch, and related multiphysics applications. Develop, implement, and improve physics-based simulation capabilities, numerical methods, and engineering workflows. Use simulation, experimental, and engineering data to analyze process and hardware behavior, identify key physical mechanisms, and support design decisions. Develop and evaluate reduced-order, surrogate, and machine-learning models where they provide practical improvements in computational efficiency or predictive capability. Build AI-enabled tools and agentic workflows that allow engineers to more effectively set up, execute, analyze, and interpret computational models. Collaborate with process, hardware, experimental, and software teams to define modeling problems, interpret results, and translate modeling insights into engineering decisions. Verify and validate modeling and AI-enabled capabilities using appropriate reference solutions, simulation benchmarks, and experimental data. Contribute to the development and maintenance of internal modeling software, reusable workflows, documentation, and best practices. Communicate modeling results, assumptions, limitations, and recommendations clearly to scientists, engineers, technical leadership, and customers. Minimum Qualifications MS or PhD in Mechanical Engineering, Electrical Engineering, Aerospace Engineering, Chemical Engineering, Materials Science, Physics, or a related field. Demonstrated experience in computational physics or multiphysics modeling, including one or more areas such as transport phenomena, fluid dynamics, heat transfer, chemistry, thermal-structural mechanics, plasma dynamics, or feature-scale process modeling. Strong background in applied mathematics and computational methods, including numerical methods, statistical modeling, uncertainty quantification, or computational solver development. Strong programming experience in Python and/or C++, with experience developing scientific or engineering applications. Experience with machine learning for scientific or engineering applications, such as surrogate modeling, physics-informed AI, neural operators, or related data-driven approaches. Experience working with simulation data and computational models to analyze complex physical or engineering systems. Strong technical judgment and attention to detail. Excellent written and verbal communication skills. Preferred Qualifications PhD degree preferred Experience with high-performance computing, parallel computing, or GPU acceleration for scientific and engineering applications. Experience with LLM-based agents, tool use, or agentic workflow orchestration for technical or scientific applications. Experience with multi-fidelity modeling, reduced-order modeling, surrogate modeling, or methods that connect models across different levels of fidelity. Familiarity with commercial, open-source, or in-house multiphysics simulation platforms such as ACE+, COMSOL, ANSYS Fluent, OpenFOAM, or similar tools. Experience developing scientific software, reusable modeling frameworks, engineering workflow automation, or digital twins. Experience applying computational modeling to semiconductor process or equipment applications, such as thermal processing, plasma processing, deposition, etching, or related multiphysics problems. Additional Information Time Type: Full time Employee Type: New College Grad Travel: Yes, 10% of the Time Relocation Eligible: Yes The salary offered to a selected candidate will be based on multiple factors including location, hire grade, job-related knowledge, skills, experience, and with consideration of internal equity of our current team members. In addition to a comprehensive benefits package, candidates may be eligible for other forms of compensation such as participation in a bonus and a stock award program, as applicable. For all sales roles, the posted salary range is the Target Total Cash (TTC) range for the role, which is the sum of base salary and target bonus amount at 100% goal achievement. Applied Materials is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law. In addition, Applied endeavors to make our careers site accessible to all users. If you would like to contact us regarding accessibility of our website or need assistance completing the application process, please contact us via e-mail at Accommodations_Program@amat.com, or by calling our HR Direct Help Line at 877-612-7547, option 1, and following the prompts to speak to an HR Advisor. 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