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Exciting, ongoing opportunity to lead innovation in power systems modelling and optimisation Join our collaborative team at CSIRO advancing net zero emissions research! About CSIRO As Australia's national
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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numerical modelling framework to simulate how light and heat interact with the target body tissue, while also incorporating neural signalling dynamics to explore how light-based stimulation affects
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the established theory, and apply the theory to a series of datasets consisting of configurations of individuals in space and time. The project will also develop and disseminate software to fit models and evaluate
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. This position will focus on one key stream: understanding and improving the interaction between EVs and roadside barriers. This will involve: Modelling EV crash dynamics with conventional and SRB barriers
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, 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
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mechanisms and transformer models. Qualifications Bachelor’s degree with First Class Honours (or equivalent) and/or a research master’s degree in computer science, AI or Computer Vision; or an equivalent
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mandatory government background checks (allow for between 4 to 8 weeks) and complete any other CSIRO requirements. The student must: a) Be an Australian citizen or Permanent Resident, or a New Zealand citizen
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nanoparticles in drug delivery or diagnostics, familiarity with cellular targeting, and use of computational tools to model nanoparticle behaviour. Experience in a regulated research environment is also
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-reality application development; modern AI techniques (such as computer vision or large multimodal language models); and/or human-computer interaction. Our industry partners are developing software