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computational tools for analyzing immunogenomics data in the context of gastrointestinal autoimmune diseases and Type 1 Diabetes. Lead the application of AI and machine learning to identify novel therapeutic
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to engage with multidisciplinary teams and external partners. Desirable attributes include experience with spatio-temporal models, machine learning, Bayesian methods, and knowledge of environmental exposure
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modelling and machine learning to join and lead a dynamic international team of early-career researchers. This exciting role is part of a cutting-edge project investigating the structural integrity
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: Developing and deploying machine learning models (e.g. graph neural networks, neural force fields, diffusion models) for molecular property prediction and molecular generation. Integrating quantum chemistry
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(working as part of a team), and attention to detail. Experience in programming multiple languages (Python, MATLAB, Simulink, C++, VBA, etc) Some experience in developing machine learning models Demonstrated
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experience in machine and/or deep learning applied to geospatial data. Demonstrated experience in the use of HPC and handling of very large datasets. Experience in software development and contribution
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. Demonstrated skills in longitudinal modelling and data synthesis, including machine learning, regression and GLM. Demonstrated ability to undertake high quality academic research in the field of dementia
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applies. 2 Fixed-term position available for up to 24 months. Flexible work arrangements can be negotiated with the right candidate. Be part of the Australian Institute for Machine Learning -- the largest
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neurocritical care research The Opportunity We are seeking a Research Fellow - Data Science professional with strong expertise in machine learning, deep learning and high-frequency physiological signal analysis
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): project Management or Project Studies (with strong computational/AI expertise) systems Engineering or Control Systems (with applications to large-scale projects) artificial Intelligence / Machine Learning