Computational Physicist Plasma Modeling, AI/ML New College Grad - Doctorate Degree (Santa Clara, Ca

J\u00adob Title \u2013 Computational Physicist \u2013 Plasma Modeling, AI/ML\n\nLocation \u2013 Sunnyvale, CA\n\nApplied Materials is the leader in materials engineering solutions to produce virtually every new chip and advanced display in the world. Our expertise in modifying materials at atomic levels and on an industrial scale enables customers to transform possibilities into reality. Our innovations make possible\u2122 the technology shaping the future. To achieve this, we employ some of the best, brightest, and most talented people in the world who work together as part of a winning team. \n\nApplied Materials is seeking a highly skilled engineer to join its computational modeling team with a strong emphasis on plasma physics. In this role, you will develop and implement advanced computational models utilizing physics-based simulation tools and AI/ML techniques. You will design models for plasma, fluid, and thermal processes, automate simulation workflows, analyze large data sets, and contribute to the development and optimization of Ace+ software. This position requires close collaboration with cross-functional engineering teams to address complex plasma-related challenges, improve product designs, and support next-generation chamber and process technology development.\n\n## Qualifications:\n\n * Advanced degree (M.S. or Ph.D.) in Mechanical, Electrical, Aerospace, Chemical Engineering, or a related field with specialization in plasma physics.\n * Proven ability to apply plasma physics, chemical kinetics and fluid flow principles to solve challenging problems.\n * Experience with commercial or academic plasma modeling tools (e.g. HPEM, ACE+, VizGlow, Comsol, etc.)\n * Experience automating simulation workflows using Python, MATLAB, or similar scripting tools for data processing and large-scale analysis.\n * Proficient in at least one programming language (Python, MATLAB, C, C++, or Fortran).\n * Excellent written and verbal communication skills for effective collaboration across engineering teams.\n\n\n\n## Desirable Qualifications\n\n * Hands-on experience applying machine learning techniques to enhance physics-based simulations, including: surrogate models. reduced-order models, uncertainty quantification\n * Familiarity with open-source machine learning libraries and frameworks\n\n\n\n## Qualifications\n\n### Education:\n\nDoctorate Degree (Required)\n\n### Skills\n\n### Certifications:\n\n### Languages:\n\n### Years of Experience:\n\n4 - 7 Years\n\n### Work Experience:\n\n## Additional Information\n\n### \n\n### Shift:\n\n10-Day 8-Hr (United States of America)\n\n### \n\n### Travel:\n\nYes, 10% of the Time\n\n### \n\n### Relocation Eligible:\n\nYes\n\n### Referral Payment Plan:\n\nNone\n\nU.S. Salary Range:\n\n$138,000.00 - $190,000.00\n\nThe 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. \n\nFor 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.\n\nApplied Materials is an Equal Opportunity Employer committed to diversity in the workplace. All 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. \n