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Using Bayesian Statistics and Machine Learning to understand grain-surface chemistry

Lead Research Organisation: UNIVERSITY COLLEGE LONDON
Department Name: Physics and Astronomy

Abstract

There is a significant amount of uncertainty surrounding grain-surface chemistry processes, despite these being speculated as being crucial to the formation of more complex molecules, such as the amino acid glycine. In this project, I will look to use Bayesian statistics and machine learning techniques to improve our understanding of these process as well as make predictions about what pre-biotic species could potentially form on interstellar dust grains.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
ST/P006736/1 30/09/2017 30/03/2026
2322240 Studentship ST/P006736/1 30/09/2019 29/09/2023 Johannes Nasim Friedrich Heyl