Prediction of molecular interactions with RNA using AI
Project overview
Project title
Prediction of molecular interactions with RNA using AI for basic research and therapeutic discovery.
Project description
This Project will develop deep-learning models to predict interactions of ribonucleic acid (RNA) with other molecules. The expected outcomes are to improve prediction capabilities to decode RNA interactions in disease mechanisms, identify novel therapeutic modalities, and improve existing therapies for targeting RNA. This could result in enhanced capacity to design new therapies and potential to optimise RNA targeting molecules for therapeutic applications.
Supervisory team
University
| Name of university supervisor | Eduardo Eyras |
| Name of university | Australian National University |
| Email address | eduardo.eyras@anu.edu.au |
| Faculty | John Curtin School of Medical Research |
CSIRO
| Name of CSIRO supervisor | Denis Bauer |
| Email address | Denis.Bauer@csiro.au |
| CSIRO Business Unit | Health and Biosecurity |
Industry
| Name of industry supervisor | Alex Gavryushkin |
| Name of business/organisation | RNAfold.AI Pty Ltd |
| Email address | alex@rnafold.ai |
Further details
| Primary location of student | The John Curtin School of Medical Research, Australian National University, Acton ACT 2601, Canberra |
| Industry engagement component location | RNAfold.AI Pty Ltd, RNA Institute at University of New South Wales, 223 Anzac Parade, Kensington NSW 2033 |
| Other locations | CSIRO Black Mountain Science and Innovation Park, Clunies Ross Street, Acton ACT |
| Ideal student skillset | Essential skills: Capacity of critical thinking and logical design of research plans Ability to develop new theoretical approaches, implement them into working computer code, and test them with real datasets Keen interest in applying new ideas to solve real world problems, and in the translation of academic research into applied and commercial environments. Desirable skills: A double degree combining experience in Computer Science, Mathematics, Data Science or related subjects with Biology, Biochemistry, Biophysics, or related areas Hands-on knowledge of Machine Learning and its underlying mathematical formulations, in particular applied to molecular problems. |
| Application Close Date | Open until position filled |
| Apply | ANU |