Data analysis and simulation for neutrino oscillation
Kam-Biu Luk, Professor
Physics
Applications for Fall 2024 are closed for this project.
We are active in a number of different areas of data analysis, using data from the DUNE near and far detector prototypes, using data from the T2K experiment, and with simulation studies in planning for the upcoming DUNE experiment. Potential analysis work includes tasks focused on the use of machine learning algorithms applied to large datasets and the use of large scale computing resources, both of which are highly transferable skills to learn, which are transferable to many other fields. We invite applications to participate in our analysis effort.
Role: Students would have the opportunity to learn software tools commonly used in nuclear, particle physics, and cosmology. The student will be provided with ample guidance during this process and will have opportunities to shift focus to different aspects of the project - high performance computing, Bayesian statistical analysis, and/or neutrino physics - depending on their interests.
Qualifications: Junior or senior physics majors with an interest in computation. Some familiarity with programming on UNIX/LINUX and C++/Python is highly desirable.
Day-to-day supervisor for this project: Cheng-Ju Lin, Callum Wilkinson, Dan Dwyers, Staff Researcher
Hours: to be negotiated
Off-Campus Research Site: Physics Division, LBNL
Mathematical and Physical Sciences