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.
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
ST/P006736/1 | 01/10/2017 | 30/09/2024 | |||
2322240 | Studentship | ST/P006736/1 | 01/10/2019 | 30/09/2023 | Johannes Nasim Friedrich Heyl |