Research Assistant in Machine Learning and Computational Materials Discovery

All vacancies of AustraliaEducation & TrainingResearch Assistant in Machine Learning and Computational Materials Discovery

Join the RMIT Theoretical Chemical and Quantum Physics research group

Summary about this job

Research & Fellowships

Company: RMIT University

Location: Melbourne

Work type: Contract/Temp

Salary: $82143 - $88172 p.a. + 17% Super

Phone: +61-2-9748-5765

Fax: +61-2-6938-9832

E-mail: n\a

Site:

Detail information about job Research Assistant in Machine Learning and Computational Materials Discovery. Terms and conditions vacancy

  • Convenient CBD Location
  • Full-Time, 2 year Fixed-Term position
  • $82,143 - $88,172 p.a. + 17% Superannuation

• Convenient CBD Location
• Full-Time, 2 year Fixed-Term position
• $82,143 - $88,172 p.a. + 17% Superannuation

Our Organisation

RMIT is a global university of technology, design and enterprise. Our mission is to help shape the world through research, innovation, teaching and engagement, and to create transformative experiences for our students, getting them ready for life and work.

The School of Science is one of RMIT's top-performing research schools, we deliver research that addresses the 'real life questions' essential to Australia's innovation agenda.

In the 2016 QS World University Rankings by Subject, RMIT is 16th in the world (highest ranked in Australia) in Art and Design, and 36th in the world (fourth highest in Australia) in Architecture and the Built Environment. We are also among the world’s top 100 universities in Engineering (Civil and Structural; Electrical and Electronic; and Mechanical, Mechanical, Aeronautical and Manufacturing); Accounting and Finance; and Business and Management Studies).

RMIT University is an Athena SWAN member and the College of Science, Engineering and Health is central to driving improvements in gender equality, diversity and inclusion, particularly in the Science, Technology, Engineering, Mathematics and Medicine (STEMM) disciplines.

The Role and Responsibilities

Join the RMIT Theoretical Chemical and Quantum Physics research group and work with Prof Salvy Russo’s team, focussing on theoretical and computational models of exciton transport, generation and recombination, optical and electronic properties of excitonic and photovoltaic materials.

Skills & Experience Required

You will have demonstrated research experience in the development and application of modern machine learning methods applied to the prediction of electronic/optical properties of photovoltaic materials, using atomic scale modelling approaches such as density functional theory (TDDFT /DFT), Quantum Chemistry methods and semi-empirical modelling methods.

An ability to define, investigate and solve complex problems in theoretical physics and data analysis using various techniques and approaches will be essential to your success in the role.

To Apply

For further information please contact Professor Salvy Russo on +61 3 9925 2601 or to view a position description visit yourcareer.rmit.edu.au and search using job reference number 564214.

PD.docx

Applications close on Wednesday 25th July 2018.

Applicants are requested to separately address the key selection criteria as outlined in the Position Description. This role will require satisfactory confirmation of a Working with Children Check.

RMIT is an equal opportunity employer committed to being a child safe organisation. We are dedicated to attracting, retaining and developing our people regardless of gender identity, ethnicity, sexual orientation, disability and age. Applications are encouraged from all sectors of the community and we strongly encourage applications from the Aboriginal and/or Torres Strait Islander community.

Responds for Research Assistant in Machine Learning and Computational Materials Discovery on FaceBook

Read all comments for Research Assistant in Machine Learning and Computational Materials Discovery. Leave a respond Research Assistant in Machine Learning and Computational Materials Discovery in social networks. Research Assistant in Machine Learning and Computational Materials Discovery on Facebook, LinkedIn and Google+