Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
data on homeowner retrofit needs and preferences. Undertaking research trials to test and refine the AI algorithms used in our platform. Meaningful assistance in research and policy development with a
-
-based algorithms (e.g., GNNs, deep reinforcement learning) design and simulate dynamic models of megaproject systems prepare and submit journal articles to high-impact publications contribute
-
development of open-source tools e.g. in Python, R, Matlab or Excel to contribute to the energy transition of Consumer Energy Resources (CER), including: Handling and analysis of large real-world datasets from
-
an opportunity for a Postdoctoral Fellow. You will contribute to UNSW’s research efforts in developing machine learning algorithm for photovoltaic applications and utilising them for the investigation
-
and innovation catalyst, in this exciting project, you will develop novel algorithms to monitor and analyse workers' movements, detect harmful movement patterns, and implement simple intervention
-
an opportunity for a Postdoctoral Fellow. You will contribute to UNSW’s research efforts in developing machine learning and deep learning algorithms for dynamic systems (sequential or time-series data). Experience
-
) implement the COMPAS survey across two waves at St John Ambulance, (c) develop a predictive algorithm that can predict suicidal intentions and behaviours 12 months later, (c) use the algorithm to stratify
-
modelling and simulation of transmission and distribution networks, including benchmarking data models, developing optimal power flow algorithms, and creating state estimation and multi-energy optimisation
-
fixed-term appointment Remuneration: 4-year scholarship package totalling approximately $47,000 per annum tax exempt (2025 rate) 4-year Project Expense and Development package of $13,000 per annum
-
novel opportunity to automate and improve the frailty assessment process, aiming for greater consistency and predictive accuracy. Aims i) Develop a deep learning algorithm to autonomously detect and