Modeling Nuclei via Machine Learning Networks

Join us in modeling a fundamental property of nuclei via the exciting new realm of Machine Learning (ML) networks! We model the nuclear mass via a novel Machine Learning algorithm, with the aim of finding underlying physical correlations in exotic, inaccessible nuclei.

Our algorithm, the Physically Interpretable Machine Learning (PIML) model, is a Python-based mixture density network model that determines the nuclear mass via a mapping of probability distributions. It is constantly being improved, updated and tinkered with, and we're looking for students to assist in its development and implementation.

Project scope is flexible, and ranges from front-end code usage to back-end algorithmic developments. While prior coding knowledge is useful, it is by no means required; regardless of your previous coding experience, we'll have a project ready to tailor to you!

If you're interested in our efforts in ML within nuclear physics, please join us!

Name of research group, project, or lab
The PIML Collaboration
Why join this research group or lab?

Semester project opportunities are primarily computational, and can be flexed to fit what you might be looking for. Hours and commitment time are also flexible; we will work with you to create something optimized to what you're looking for!

We have a wealth of ideas and projects ready for motivated students; opportunities to lead a project push are readily available, including semi-independent investigation and potential publication

Our group consists of multiple faculty members and students who are all ready to work with you; you'll be joining a lively group of research peers!

Representative publication
Logistics Information:
Project categories
Physics & Astronomy
Student ranks applicable
First Year
Sophomore
Junior
Senior
Student qualifications

While previous experience in physics and coding is useful, it is by no means required. All we ask is for an excitement and willingness to learn!

Hours per week
1 credit / 3-6 hours
2 credits / 6-12 hours
3 credits / 12+ hours
Compensation
Research for Credit
Unpaid - Volunteer
Number of openings
1
Techniques learned

Coding (primarily Python) skills, machine learning

Project start
Whenever you are available
Contact Information:
Mentor
wporter@nd.edu
Ph.D Student
Name of project director or principal investigator
Sam Porter
Email address of project director or principal investigator
wporter@nd.edu
1 sp. | 3 appl.
Hours per week
1 credit / 3-6 hours (+2)
1 credit / 3-6 hours2 credits / 6-12 hours3 credits / 12+ hours
Project categories
Physics & Astronomy