HPC-AI has now become a bundled term due to the rapid progress of deep learning architectures, which are empowered by running AI applications at scale on supercomputing infrastructures. NCI empowers a wide range of research that needs computing, storage, and cloud resources through various merit-based allocation schemes. However, most PhD students are not eligible as independent lead CI to apply for the national scheme. To address the growing demand for computing and storage resources of the HPC-AI application, as well as supporting our next generation of researchers, NCI offers 100KSU with appropriate storage and AUD$10,000 per year for each successful application for eligible PhD project costs to support PhD programs through the NCI Australia HPC-AI Talent Program.
NCI supports AI applications on diverse scientific problems such as solving differential equations, accelerating molecular dynamics, predicting 3-dimensional protein structures, controlling nuclear fusion, or weather forecasting with higher resolution and accuracy. Some HPC-AI applications encounter a range of workflow bottlenecks. The performance can be limited by processes that are compute bound, I/O bound, memory bound, or a combination of the above, pushing the need for HPCD into the exascale range. The unprecedented representational ability of deep neural network structures can only be brought to bear on the most complex and urgent global problems by model training with ultra-large datasets on heterogeneous exascale HPCD architectures.Through this program, we will identify implementation hurdles and barriers, provide support to address them for the next generation of young researchers who in turn will empower their future organisations for the broader benefit of the Australian research and industry sectors.
$10,000 and 100KSU with appropriate storage per annum 2023
NCI will support up to 10 PhD students for 1-year supplements to their PhD program in 2023. Additional project funds and compute resources should be used within the year of the award.
NCI intends to run the program again in 2024 and beyond. Only one award will be made per individual, as it is expected that participants will become competitive in NCI’s merit allocation schemes (particularly NCI Adapter, or joining a supervisor’s application to NCMAS) following the award.
Should you have any questions, please direct them to email@example.com.
Eligible candidates must apply through the application process outlined below for the scholarship program. Incomplete applications will not be considered.
Selection is merit-based by an independent Assessment Committee. The Assessment Committee’s decision is final and cannot be appealed.
|20 Dec 2022||Applications open|
|31 Jan 2023||Applications close|
|1 – 22 Feb 2023||Assessment Committee review and decision|
|Extended 23 Feb 2023||Scholarship result announcement|
Please provide your resume (2 pages maximum), two reference letters (each 1-page maximum) and your application (2 pages maximum) to firstname.lastname@example.org as PDF attachments. Please use the Application Template file below.
For successful scholarship recipients, program reviews are conducted each quarter for the duration of the program and a final report is required at the end of the program.
Onboarding with NCI training
Project plan initial review
|May 2023||Quarter 1 review|
|Aug 2023||Quarter 2 review and mid-term showcase|
Quarter 3 review
|Jan 2024||Final report|
|Feb 2024||End of program presentation and symposium|
More details to be released. Subscribe to the NCI newsletter to stay up to date.
Tanvir Saurav is a PhD student in the Bushfire Research Group, School of Science, UNSW Canberra.
He was born and raised in Bangladesh. After high school, he moved to the US where he completed a B.S. in Mechanical Engineering from Temple University in 2018 and joined the Master of Philosophy (M.Phil.) program at The University of Melbourne where he worked on direct numerical simulation of rough-wall turbulent flows using high-performance computing.
He joined the PhD program at UNSW Canberra in 2022. His current research areas are turbulence, computational fluid dynamics, and high-performance computing. He uses numerical methods and supercomputers to model and simulate ember transport in bushfires.
Ke Ding is a PhD student from the Wen Group at the John Curtin School of Medical Research. Ke's research projects focus on building DL models to uncover the interaction among RNA binding proteins and summarise transcription factor binding sites' characteristics, and he has been using Gadi's GPU nodes to train deep neural networks. In addition, since the size of genomic data is enormous, he has been using Gadi's CPU nodes to process the big data.