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Up Directory 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/

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Modified: Thu Feb 27 15:44:57 2020 GMT
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