Thesis title: Statistical analyses of astrochemical Big Data.
Supervisor: Prof. S. Viti (University College of London)
Co-Supervisor: Prof. J. Yates (University College of London)
Recruitment Institution: University College of London, UK
Doctoral School: University College of London, UK
Mobility: The ESR will spend 6 months at Mellanox Technologies under the supervision of Dr. C. Bridger.
Eligibility: European and non-European students who not have resided or carried out their main activity (work, studies, ect.) in United Kingdom for more than 12 months in the 3 years immediately before the recruitment date.
The study of how stars and planets form from molecular clouds heavily relies on our understanding of how molecules form and are destroyed in the ISM. We now have a wealth of observational and modelling data that allow us to use molecules as tools to determine the physical and chemical conditions of star forming regions. What we lack is the best tools to derive the right answers from such data: the latest ground and space submillimeter observatories are producing datasets that are so large that searching for answers out of these massive data sets has become a problem that can not be tackled any longer by traditional technologies. While simple statistical methodologies have been used for decades, the astrochemical community has yet to come up with a novel approach based on high-level statistical and machine learning techniques routinely used in other fields. The proposed project involves the production of a self-consistent set of statistical and Machine Learning tools that will allow the Astrochemistry Community to extract the physical and chemical conditions of stellar nurseries from large datasets obtained by submillimeter and far infrared observatories. This project will be in collaboration with the company Mellanox.
The thesis is part of the ACO network, whose ultimate goal is to reconstruct the early history of the Solar System by comparing presently forming solar-type planetary systems with its small bodies. The comparison will be based on the most advanced astrochemical knowledge, which will be developed by the interdisciplinary ACO team.
Requested background: The successful applicant must have a Master degree in Physics or Astrophysics by the time of enrollment as well as satisfy the UCL language requirements as detailed here. Previous experience in programming (in any language) is strongly desirable. Team work ability is essential.
Salary: The gross amount of the Research fellowship is paid as follows:
- 36 months: £ 37,730 to £ 46,950 per annum which includes family and mobility allowance where applicable.
See details on the financial aspects of Marie Sklodowska-Curie-ITN in the Guide for applicants Marie Sklodowska-Curie Actions Innovative Training Networks (ITN) 2018, published at http://ec.europa.eu/research/participants/data/ref/h2020/other/guides_for_applicants/h2020-guide-appl-msca-itn_en.pdf.
How to apply
How to apply
Send an email to the address firstname.lastname@example.org, The mail should include a letter of interest, a CV in pdf format and at least one recommendation letter.
Closing date for application: 2 June 2019
Expected date of recruitment: within 1 September and 1 November 2019.