Location: Los Alamos, NM
Cap-Exempt H-1B Position — No lottery required
**What You Will Do** The Adaptive Machine Learning team is seeking a post-bachelor student in computational physics. The team is part of the Accelerator Operations and Technology (AOT) Division within the Instrumentation and Control (IC) Group. Our work focuses on research and development to improve accelerator operations through machine learning, control theory, and physics-based simulation. Beyond research and development, the team supports the implementation of practical, operationally relevant solutions for the Los Alamos Neutron Science Center (LANSCE). The successful candidate will contribute to the computational aspects of this research. In particular, the candidate will support efforts to improve accelerator optimization and tuning by modernizing existing software, applying physics-constrained machine learning, or developing differentiable physics simulations. The position is for one year, renewable for a second year. Please apply before July 15th, 2026. **What You Need** **Minimum Job Requirements:** - Proficiency in Python. - Able to write clear, documented code. - Able to communicate effectively. - Able to perform research independently. - Experience working in a research environment. **Desired Qualifications:** - Demonstrated proficiency in Python and C++; experience with GPU computing is a plus. - A good understanding of classical mechanics and electromagnetism. - Knowledge of the physics of particle beams and/or plasmas. - Experience in nuclear, particle or accelerator physics research. - Experience with machine learning and/or differentiable physics simulations. **Education/Experience** - Must have a bachelor's degree in physics, mathematics, computer science, or related field at the time of starting the position from an accredited university. Degree must have been obtained within 3 years of hire date. - Must have graduated with a cumulative GPA of 2.75 or above on a 4.0 scale (or equivalent). - Not to have accepted or be enrolled in a graduate progra