What path to follow for scientific computing?

In summary, if you are interested in combining your interests in computer science and astronomy/physics, you could pursue graduate studies in numerical analysis/scientific computation, which is often offered in either computer science or applied math programs. UNC Chapel Hill offers a graduate program in Applied and Computational Mathematics, and their CS department also has researchers involved in high-performance scientific computing. This could be a potential path for you to work with scientists and researchers while also utilizing your computer science skills.
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I am 26 years old and currently working on my B.Sci. in Computer Science. I am also interested in astronomy and physics as well, and my dream job would be something where I can combine those interests and work alongside scientists and researchers. I love science and as long as I make a reasonable income, I don't care about the money - I just love the intellectual pursuit.

Here in North Carolina, UNC Chapel Hill and NC State University both offer graduate programs in astronomy/astrophysics. NCSU offers a graduate degree in data science/analytics as well.

What kind of path should I look into if I want to work with scientists but coming from a computer science / information technology background? Would it be possible to move into a physics graduate program if I essentially minored in physics during undergrad? Or would I have to double major?

Basically, I'm trying to figure out what to do while I'm in undergrad to simultaneously help my chances of getting into graduate school while also allowing me to find a job soon after undergrad (as I will likely be getting married and purchasing a house in the next 5 years). I am already working with a physics professor on a web application that he is going to publish in a physics education journal - maybe that + his recommendation will help with graduate school admissions?
 
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  • #2
Since you already come from a computer science background, the easiest path to continue to collaborate and work with scientists would be to pursue graduate studies in numerical analysis/scientific computation, which are typically offered in either the computer science or applied math programs. Since you mentioned UNC Chapel Hill, they offer a graduate program in Applied and Computational Mathematics, offered through the Math Department.

http://math.unc.edu/research/applied-and-computational-mathematics/

I also see that the UNC CS department have researchers involved in high-performance scientific computing:

http://cs.unc.edu/research/areas/high-performance-computing/
 

1. What is scientific computing?

Scientific computing is the use of computers to solve complex scientific problems and perform data analysis. It involves using mathematical and computational techniques to model, simulate, and analyze scientific phenomena.

2. What are the benefits of using scientific computing?

There are many benefits to using scientific computing, including increased efficiency and accuracy in data analysis, the ability to handle large and complex datasets, and the ability to perform simulations and experiments that may not be possible in real life.

3. What skills are needed for scientific computing?

To be successful in scientific computing, you need a strong background in mathematics, statistics, and computer science. You should also have experience with programming languages, data analysis tools, and scientific software.

4. What are some real-world applications of scientific computing?

Scientific computing is used in a wide range of fields, including physics, chemistry, biology, engineering, and economics. It can be used to model weather patterns, predict the behavior of complex systems, design new drugs, and optimize industrial processes, among many other applications.

5. What are some resources for learning about scientific computing?

There are many online resources available for learning about scientific computing, including online courses, tutorials, and forums. You can also find textbooks and reference materials on topics such as numerical methods, data analysis, and programming languages commonly used in scientific computing.

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