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computational methods for modelling social dilemmas that can account for real-world complexity in agents’ behaviour. We will build on novel computational techniques to produce realistic enough models that can be
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would develop new computational methods for modelling Groupthink that can account for real-world complexity in agents’ behaviour, and build realistic enough models that can fit past and present empirical
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systems and swarm robotics. The project builds on well established computational and mathematical modelling techniques to achieve its aims. Departure points will be agent-based simulations, optimisation
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of Machine Learning (ML) models across large-scale distributed systems. Leveraging advanced AI and distributed computing strategies, this project focuses on deploying ML models on real-world distributed
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of experiments, analysis of neuropsychological and cognitive data and application of computational models. It also contributes to scientific publications and supports collaboration within a network of researchers
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people who discover them The Opportunity The Research Fellow will be a motivated and talented computational scientist and a key member of Monash University ice sheet modelling research team and SAEF’s
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science, computational chemistry, computational biology, or a closely related discipline. Demonstrated experience in the design of machine learning architectures such as GNNs, VAEs, or diffusion models applied
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programming (R or Stata preferred), and scientific computing Excellent problem-solving, planning and written communication skills Experience working with health-related data and modelling methods A
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cutting-edge AI methodologies, focusing on combining data-driven approaches with physics-informed models to tackle challenges in MRI reconstruction. By integrating MRI acquisition physics directly into
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models for deployment in real-world driving scenarios. Required knowledge First-class bachelor’s honours or master’s degree in computer science, engineering, or a related field. Alternatively, second upper