Do you have a PhD in Computational chemistry/physics or similar?
Work with world-leading scientists and simulate the properties of a range of perovskite materials
Join CSIRO’s Data61, the largest data innovation group in Australia
CSIRO’s Data61 is seeking to appoint an Experimental Scientist to work within their Modelling and Simulations group.
As the successful candidate, you will work with world-leading scientists on the development of a new industry-specific materials informatics platform for screening and designing metal oxide materials for a next generation solar redox reactor. You will draw on large data bases of computational results and undertake large numbers of simulations using density functional theory, to create a comprehensive resource for multivariate analytics and machine learning.
Your duties will include:
Engagewith experimental partners and international collaborators to create a database of existing results suitable for screening and processing using established thermochemical methods.
Simulate the properties of a range of perovskite materials using density functional theory.
Predictthe structure/property relationships of perovskite materials using the most appropriate and reliable suite of machine learning methods.
Contribute tothe development of a bespoke materials informatics platform that can be repurposed in the future as other opportunities arise.
Undertake regular reviews of relevant literature and patents.
Location: Docklands VIC Salary: AU$83k – AU$94k plus up to 15.4% superannuation Tenure: Specified term of 18 months Reference: 67331
To be considered you will need:
A doctorate in a relevant discipline area, such as computational chemistry, computational physics or materials data science.
A detailed knowledge and extensive experience in the simulation of materials using density funtional theory (DFT)
Demonstrated proficiency using high performance computing environments.
Proven experience, as evidenced by examples, in machine learning and data science methods and their application to large data sets.