Analysis of physics data using deep learning methods

Required Availability
The End of Time
Course Credit?
Yes - PH495
Paid Position?
No
Preferred Majors
Computer Science | Physics | Applied Mathematics | Mathematics
Faculty
Igor Ostrovskiy
Keywords
numerical analysis | neutrino physics | physics | particle physics
Description

Students can work on the following projects: 1. Novel approaches to simulation of nuclear track detector data for the MoEDAL experiment at the Large Hadron Collider using Generative Adversarial Networks 2. Position reconstruction of gamma and beta decays in the EXO-200 neutrinoless double-beta decay experiment using Convolutional Neural Networks Fluency in programming languages (Python and/or C, Linux OS) are required. Familiarity with deep learning technique and relevant software (PyTorch, Keras) are preferred. Access to GPU computing system will be provided

Contact Phone #
(205)348-3773
Contact Email
iostrovskiy@ua.edu
Research Website
http://iostrovskiy.people.ua.edu/
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