Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Monash University
- Curtin University
- University of Adelaide
- Queensland University of Technology
- Swinburne University of Technology
- La Trobe University
- University of Southern Queensland
- Flinders University
- Murdoch University
- Nature Careers
- University of Melbourne
- CSIRO
- Charles Sturt University
- Data61 PhD Scholarships
- James Cook University
- The University of Newcastle
- 6 more »
- « less
-
Field
-
structural health monitoring, especially on computer vision, image processing, machine learning, deep learning, signal processing and data analysis techniques, are preferred. Application process To apply
-
algorithms and deep learning models. Have proficiency in Python in a Linux environment and development experience using Tensorflow or PyTorch. Have strong linear algebra and computer vision knowledge. Have
-
, especially in ultracold quantum gases or condensed matter theory Proven analytical, computational, and modelling skills Experience with numerical simulations of quantum or many-body systems A deep curiosity
-
PhD student(s) will join a vibrant team of postdocs, academics, and up to four PhD students working collaboratively across modelling, qualitative fieldwork, and optimisation techniques. PhD Research
-
in the design, implementation, evaluation and dissemination of quality integrated health promotion programs. Located within CERIPH, the Drowning Prevention Evidence and Evaluation Project (DEEP) works
-
, especially in ultracold quantum gases or condensed matter theory Proven analytical, computational, and modelling skills Experience with numerical simulations of quantum or many-body systems A deep curiosity
-
prevention research. DEEP was cited in a recent a bibliometric analysis 1995–2020 as an emerging research cluster in drowning prevention research. DEEP works towards the goals of the WA Health Promotion
-
to supplement a scholarship. Learn more about Mark Govier and why he created this scholarship
-
. The specific research areas we will explore are + Adaptive scientific deep learning methods for mathematical physics problems governed by partial differential equations (domain decomposition, adaptive quadrature
-
materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for