2-year post-doctoral position in Machine Learning and Computational Materials Discovery of Photovoltaic Materials

Applications are welcomed for a 2-year post-doctoral position in Machine Learning and Computational Materials Discovery of Photovoltaic Materials. The research will be carried out at RMIT University in Melbourne Australia under the direction of Professor Salvy Russo.

This position is funded by the ARC Centre of Excellence in Exciton Science (ACEx), which is a newly established research centre, which has received $31.85 million in funding by the Australian Research Council.

This research centre links research teams at RMIT University, the University of Melbourne, Monash University, UNSW and the University of Sydney. Its partners include: The Defence Science and Technology Group, The Reserve Bank of Australia and CSIRO.

The primary mission of ACEx is to manipulate the way light energy is absorbed, transported and transformed in advanced molecular materials. The Centre programmes span high-throughput computational screening, single molecule photochemistry and ultrafast spectroscopy and embrace innovative outreach and commercial translation activities. The Centre plans to capture the knowledge generated as new intellectual property, materials processing know-how, high-impact publications and through the creation of new employment opportunities. The expected outcomes and benefits include new Australian technologies in solar energy conversion, energy-efficient lighting and displays, security labelling and optical sensor platforms for defence.

Further details regarding the Centre of Excellence can be found at

www.excitonscience.com

The appointee will join the RMIT Theoretical Chemical and Quantum Physics research group, with strong interactions with other research groups and institutions working under the ARC Centre of Excellence in Exciton Science (ACEx). The appointee to this position will work with Prof Salvy Russo’s team, focusing on materials discovery of new photovoltaic materials using machine learning methods and abinitio modelling techniques.

Job Requirements: To be eligible to apply, candidates must have a PhD in Physics or Theoretical Physical Chemistry and Research experience in the development and application of modern machine learning methods applied to the prediction of electronic/optical properties of photovoltaic materials

Candidates should also have:

  1. A Demonstrated ability to define, investigate and solve complex problems in theoretical physics and data analysis using various techniques and approaches as required.

  2. Excellent mathematical and/or computer programming skills.

  3. High level interpersonal, written and oral communication skills.

  4. Ability to work autonomously whilst displaying a strong commitment to work in a team environment, including the demonstrated ability to confidently and effectively work with colleagues and external collaborators.

  5. Excellent publication record relative to opportunity.

How to Apply Applicants can lodge an application by following the instructions given in the following weblink:

http://yourcareer.rmit.edu.au/caw/en/job/564214/research-assistant-in-machine-learning-and-computational-materials-discovery

Please also send your CV to Prof Salvy Russo at This email address is being protected from spambots. You need JavaScript enabled to view it.

Applications close on 31 May 2018 11:55 PM AUS Eastern Standard Time.

For further information please contact Prof Salvy Russo at This email address is being protected from spambots. You need JavaScript enabled to view it.