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different areas of mathematics. One position will focus primarily on geometric and structure-preserving methods for mathematical models of physical systems, including topics such as geometric numerical
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the development of novel and innovative numerical techniques aiming at improving the integration between numerical simulations and geometric modelling and processing. Within the project «FLASh: Fast simulation
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structure-preserving methods for mathematical models of physical systems, including topics such as geometric numerical integration, finite-volume and finite-element methods, variational formulations
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& Geometric Models, Low Effective-dimensional Learning Models, Implicit Regularization, and Reinforcement Learning through Stochastic Control (a brief description of each these is as follows (additional details
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: Topologically-Informed Large Language Models for Innovation Intelligence. The project is funded by FFplus, a EuroHPC Joint Undertaking initiative under the European Union's Digital Europe Programme, and brings
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, training, and evaluating neural-network models with a modern framework such as PyTorch or JAX. Knowledge of graph neural networks, geometric deep learning, invariant or equivariant models, or related
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), ideally geometric/graph neural networks, equivariant models, or generative models. Interest in applying AI to molecular or biological problems; prior structural biology experience is a plus but not required
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Full time, fixed term position for two years. Located at our Camperdown campus. Unique opportunity to join the School of Mathematics and Statistics to work on the project "Geometric methods
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sequence and to model the evolution of body size using bounded evolutionary models. 3) Biomechanical and Hydrodynamic Modeling: Apply computational fluid dynamics to test hydrodynamic hypotheses related
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Statistics to work on the project "Geometric methods to detect rate-induced tipping events" Base Salary $117,936 (Level A, Step 6) per annum + 17% superannuation About the opportunity We are seeking to appoint