|CCL 20.02.27 PhD position: Modelling the Formation and Growth of Atmospheric Molecular Clusters using Quantum Chemical and Machine Learning Methods|
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Date: Thu Feb 27 10:25:51 2020
Subject: 20.02.27 PhD position: Modelling the Formation and Growth of Atmospheric Molecular Clusters using Quantum Chemical and Machine Learning Methods
Applications are invited for a PhD fellowship/scholarship at Graduate School of Science and Technology, Aarhus University, Denmark, within the Chemistry programme. The position is available from 1 Aug 2020 or later. Title: Modelling the Formation and Growth of Atmospheric Molecular Clusters using Quantum Chemical and Machine Learning Methods. Research area and project description: The formation and growth of atmospheric molecular clusters constitute one of the largest uncertainties in global climate modelling. This project aims at studying these processes using quantum chemical methods and machine learning algorithms to bridge the persistent gap between theory and experiments. The project involves, but is not limited to, the following three subprojects: 1) - Cluster Formation: Studying new multi-component atmospheric cluster systems using quantum chemical methods to establish which clusters are stable under atmospheric conditions. A palette of quantum chemical methods and kinetics modelling will be applied. 2) - Machine Learning: Utilize quantum chemically derived atmospheric cluster data to develop machine learning methods. Involves applying algorithms such as kernel ridge regression and gaussian process regression. 3) - Cluster Growth: Apply machine learning methods to study cluster sizes previously out of reach using quantum chemical methods and up to measurable sizes (~2 nm). All data will be implemented in an atmospheric transport model to simulate realistic atmospheric conditions in a wide variety of regions. In all three projects we will have extensive collaboration both in-house and with national and international research partners. The project will be supervised by Assist. Prof. Jonas Elm and Professor Ove Christiansen. The project is funded by the Sapere Aude Initiative by the Independent Research Fond Denmark. Qualifications and specific competences: The candidate should have (or soon complete) a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree. The degree should be within chemistry, physics or related fields. Additional qualifications for this project: Experience with electronic structure theory methods. Basic knowledge of computer programming (e.g., Python, Matlab). Explicit knowledge about atmospheric chemistry is not required, but will count positively in the assessment. Place of employment and place of work: The place of employment is Aarhus University, and the place of work is the Department of Chemistry Langelandsgade 140, 8000 Aarhus C. Contacts: Applicants seeking further information are prior to submitting their application encouraged to contact: Assistant Professor Jonas Elm (jelm[at]chem.au.dk) For information on how to apply: https://phd.scitech.au.dk/for-applicants/apply-here/may-2020/modelling-the-formation-and-growth-of-atmospheric-molecular-clusters-using-quantum-chemical-and-machine-learning-methods/NOTE THAT E-MAIL ADDRESSES HAVE BEEN MODIFIED!!!
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