​​Scientific computing is a key tool for research in the physical sciences. Modern scientific research relies heavily on a number of highly specialized computational methods and simulation codes. In order to engage in theoretical physics, astronomy and chemistry research, some amount of computing knowledge is necessary.

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Unfortunately, many of these essential techniques are generally only taught at the graduate level, if at all. This unnecessarily high barrier to entry prevents potential undergraduate researchers from pursuing research in computational physics, astronomy and chemistry.

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This series of workshops aims to provide a crash-course in a diverse set of computation tools and algorithms that will prepare our students to engage in computational research. The workshop series focuses on building a Python-based computational toolkit that includes simulation methods and algorithms such as Monte Carlo methods and differential equation solvers, in addition to data-driven methods like machine learning. Our central priority is to train the next generation of scientists in a supportive, collaborative learning environment.