Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Monash University
- University of Adelaide
- Curtin University
- The University of Queensland
- University of New South Wales
- RMIT University
- Queensland University of Technology
- James Cook University
- La Trobe University
- RMIT UNIVERSITY
- The University of Western Australia
- University of Tasmania
- University of Melbourne
- University of Southern Queensland
- Australian National University
- Macquarie University
- Nature Careers
- Swinburne University of Technology
- AUSTRALIAN NATIONAL UNIVERSITY (ANU)
- Amgen Scholars Program
- CSIRO
- Deakin University
- Flinders University
- Murdoch University
- The University of Newcastle
- UNIVERSITY OF SYDNEY
- UNIVERSITY OF WESTERN AUSTRALIA
- University of South Australia
- 18 more »
- « less
-
Field
-
area of expertise. You may be a great fit if: You are a passionate researcher with a PhD in Computer Science or a related field, experienced in machine learning for spatial data management, with a track
-
-atomic potentials using a combination of classical and machine-learning (ML) approaches (and a new hybrid method recently developed in our group). Some of the types of simulations that will be performed
-
government background checks (allow for between 4 to 8 weeks) and complete any other CSIRO requirements. Selection criteria To be eligible applicants must: Have a basic understanding of machine learning
-
. Desirable: Proficiency in scientific programming (e.g. Python) and familiarity with data science and machine learning techniques. Experience with geochemical analytical techniques and working in a laboratory
-
on site/WFH options and flexible start/finish times, and genuine career progression opportunities via the academic promotions process. About You Completed PhD or equivalent in Design or equivalent
-
splitting and C–N coupling reactions. Work includes computational modeling of carbon-based materials, conducting simulations to understand reaction mechanisms, and developing and applying machine learning
-
campuses of the University About You The Learning and Teaching Coordinator maintains and develops administrative systems, processes and practices with a view to continually improving the provision of support
-
This PhD project is part of a larger project that aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain
-
publications and research experiences in structural dynamics and structural health monitoring, especially on computer vision, image processing, machine learning, deep learning, signal processing and data
-
techniques and associated tools (examples include, but are not limited to machine learning, density-functional-theory, materials informatics, finite-element modelling, phase-field modelling), and demonstrated