MS Computational Physics - University of A

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
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
Positions linked to scientific computing applications in diverse industries
Additional roles:
Biomedical and chemical engineers,

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
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.