PhD in Physics & Astronomy: Machine Learning from Complex Disk Models (ESR9)
Molecular emission features observed in the near and mid infrared, for example with the VLT and JWST, will allow us to determine the chemical composition of the gas in protoplanetary disks in the planet-forming regions within 10 au around new-borne stars. This project will combine our previous expertise in modelling the line radiative transfer, chemistry and heating/cooling balance in disks, see Woitke et al. (2016, A&A 586, 103) with new machine learning techniques developed in the exoplanet community, e.g. Zingales & Waldmann (2018, AJ 156, 268). Neutral networks (NNs) will be trained on the predictions by tens of thousands of complex thermo-chemical 2D disk models, where we will apply the radiative transfer code FLiTs (Woitke et al. 2018, A&A 618, 57) to post-process the ProDiMo results to identify the spectral signatures. Using an algorithm developed for the ARCiS code (artful modelling of cloudy exoplanet atmospheres, author M. Min), these NNs will enable us to retrieve the chemical composition and the physical disk parameters, including their errorbars, from the observations. We can then use these new machine learning algorithms to quickly predict the emergent near-mid infrared line emission spectra from disks as function of physical parameters like UV irradiation, dust/gas ratio and element abundances, capable to thoroughly fit and analyze JWST data to determine the physical disk parameters and their observational uncertainties, taking into account all degeneracies.
This project is part of the Marie Sklodowska-Curie Innovative Training Network (ITN) CHAMELEON “Virtual Laboratories for Exoplanets and Planet Forming Disks”. The ITN combines the expertise of eight European research institutes (Universities of St Andrews, Groningen, Copenhagen, Edinburgh, Leuven and Antwerp, the Max-Planck Institute in Heidelberg and the Netherlands Institute for Space Research) to cover all relevant aspects for this complex modelling task, joining the expertise in planetary atmospheres and protoplanetary disks, including observation and interpretation. For a complete list of all open PhD positions within this training network please visit http://chameleon.wp.st-andrews.ac.uk/
The selected PhD students will be offered a fully funded PhD place at the University of St Andrews’ Centre for Exoplanet Science with training secondment for this position foreseen at the University of Copenhagen, with additional short training at the University of Groningen. The PhD student will receive a double degree from St Andrews and from Copenhagen. The funding will be commensurate to the standard scale for PhD students in according to the Marie-Curie funding rules. The successful PhD applicants will have to register at, and comply with, the regulations of the St Leonard’s Postgraduate College at the University of St Andrews and the rules from the University of Groningen. The successful PhD applicants will follow a doctoral programme including personal training in management, science communication, and teaching.
We seek an excellent student with a strong background in physics or astrophysics. Successful candidates must hold a Masters degree or equivalent by the starting date of the position. Previous research experience on machine learning, astrochemistry and/or radiative transfer, and a track record of team work/mobility will be important criteria for the selection. This is a computational project: some prior knowledge of coding would be useful (e.g. Python and Fortran). Note that the general eligibility and mobility rules of Marie Sklodowka-Curie Actions apply.
Eligibility: CHAMELEON PhD studentships are open to people of any nationality. However, the Marie S. Curie Actions have two strict eligibility criteria for applicants to these positions:
1. EARLY STAGE: The applicant must be within the first four years (full-time equivalent research experience) of her/his research career (starting from the moment you obtain a degree that makes you eligible to study for a PhD) and not have a doctoral degree. Adjustments can be made for career breaks
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2. MOBILITY: The applicant must not have resided or carried out her/his main activity (e.g. work, studies) in the country where she/he has been recruited for more than 12 months in the three years immediately before the recruitment date (this is the day on which you start your PhD).
Further information about eligibility criteria, the application process and the online application form are available at: http://chameleon.wp.st-andrews.ac.uk/recruitment/
Deadline: 3 February 2020
Submit applications to: https://www.st andrews.ac.uk/physics/prosp_pg/index.php
For the first 3 years of employment, as Marie Sklodowska-Curie Fellows, you will receive generous benefits, including a fixed salary/living allowance and mobility/ family allowance HYPERLINK "https://ec.europa.eu/research/participants/data/ref/h2020/wp/2018-
School of Physics & Astromomy
Fixed Term: 4 years, fully funded
Start Date: Autumn 2020 PhD Entry
PhD in Physics & Astronomy: Machine Learning from Complex Disk Models (ESR9)