MS Computational Physics

Program Overview

The MS in Computational Physics at the University at Albany is a 33-credit program that equips students with the skills to solve real-world physics problems using numerical simulations and computational modeling. The program includes core courses in physics, electives in scientific computing and data analysis, and a research component. Graduates are prepared for careers in scientific research, industry, and academia.

Program Outline

Degree Overview:

This 33-credit graduate program equips you with the skills and knowledge to solve real-world physics problems using numerical simulations and computational modeling. You'll delve into various fields like medical imaging, astrophysics, optics, electrodynamics, quantum mechanics, and statistical mechanics, while mastering crucial tools like scientific computing, programming, and advanced data analysis. This program also allows non-degree students to transfer up to 12 credits towards the master's degree or transfer courses and credits into the Ph.D. Physics program for current MS students. This program aims to:

• Develop students' proficiency in scientific computing and programming using multiple languages.
• Enhance critical thinking and problem-solving abilities through computational simulations.
• Hone students' analytical skills through data exploration and visualization of results.
• Provide a platform for research collaboration in diverse areas like biomedical imaging and particle physics.
• Prepare graduates for diverse careers in scientific research institutions, industry, academic sectors, as well as pursue doctoral-level studies.

Outline:

#Program Structure:

The MS Computational Physics program consists of:

• 18 credits dedicated to core Physics courses, covering electrodynamics, quantum theory, statistical and classical mechanics, computational methods, and quantum information
• 9 elective credits delving into specific areas of study: scientific and high-performancecomputing, Bayesian data and error analysis, machine learning, advanced statistical models, or other specialized courses offered by Computer Science, Mathematics, and Electrical Engineering departments.
• 3 research credits acquired by either successfully writing and defending your final thesis OR through an exam conducted on your final project.

Courses:

#Sample Modules:

• Computational physics courses:
• Bayesian analysis
• Data mining, machine learning
• High-throughput computing
• Time/Fourier analysis for data interpretation
• Statistical mechanics and model analysis
• Research:
• Participate as part of faculty-led groups working in:
• Theoretical particle, cosmology, astroparticle physics
• Neutrino research
• X, gamma-ray space research
• Develop expertise by:
• Designing & writing physics code
• Exploring large scientific and real-word data
• Preparing and sharing research outputs
• Physics core courses:
• Classical physics and electrodynamics and quantum theory (including computational and coding elements)
• Focus areas
• Biomedical, high energy & particle
• Statistical, soft matter, optical
• Materials Science
• Develop proficiency in computer-related approaches to research
• Use the thesis/dissertation or final project as evidence of acquired skill
• Teaching:
• Employ diverse teaching methodologies
• Interactive lectures with real-time problem solving
• Project & computer/code-driven assignments
• Active student involvement and faculty interaction
• Utilize innovative computational tools
and languages (Python, Fortran, etc.)
• Emphasize coding development & testing for accurate data analysis
• Teach collaboration in scientific exploration with research-specific data
• Careers:
• Physics research positions at institutes like Lawrence Berkeley National Lab
• Engineering roles for companies like Global Foundries
• Academic positions as professors or researchers
• Modelling analyst, Machine
Learning engineer for various sectors
• Positions linked to scientific computing applications in diverse industries
• Additional roles:
• Biomedical and chemical engineers,
aerospace technician

Research Opportunities

• Participate in faculty research, gaining practical experience and expertise
• Develop coding proficiency
• Access advanced scientific computing resources
• Work with large and complex data sets

Student Learning Outcomes

• Gain mastery in classical & quantum physics, statistical mechanics
• At the curriculum level
(See full description in linked source below) and research/ thesis level
• Supplemental Programs Available

Conclusion:

This MS program equips graduates not only with strong foundational knowledge in physics & advanced computing techniques, but also the research experience necessary to excel in competitive STEM careers or further academic pursuits.