18 molecular-modeling-or-molecular-dynamic-simulation-"Prof" PhD positions at University of Adelaide
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prediction, signal tracking, fluid dynamics, and space exploration. Advancing Signal Modelling with Physics-Informed Neural Networks This project aims to develop Physics Informed Neural Networks (PINNs
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is working to be the state’s world-class cancer research institute, jointly resourced by the Federal Department of Health, CALHN and the University of Adelaide. Reporting to Associate Prof. Robin Hobbs
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: Prof David Lewis and A/Prof Phil van Eyk School of Chemical Engineering School of Chemical Engineering Email: david.lewis@adelaide.edu.au ; philip.vaneyk@adelaide.edu.au Applying: Expression of
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is likely to be tax exempt, subject to Taxation Office approval. Enquiries: Contact Person: A/Prof John Semmler School of Biomedicine Tel: (08) 8313 7192 Email: john.semmler@adelaide.edu.au
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. Fixed term, full time position available for 2 years with a possible of extension. Train with an amazing group of molecular biologists focussed on addressing critical gaps in our understandings
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the structure and function of chromatin-associated complexes involved in gene repression. About Us: We are a dynamic and international team working within the South Australian immunoGENomics Cancer Institute
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fracture outcomes using a static locked femoral nail or a femoral nail in the dynamic mode with a sliding lag screw. Clinical factors (including analgesic requirements, Time up and go) and radiological
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cutting-edge research in biochemistry, molecular biology and plant science. The student will be supervised by Dr Tatiana Soares da Costa based at the University of Adelaide. Dr Tatiana Soares da Costa is
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AEMO’s annual General Power System Risk review AEMO must model the power system at a 5 year ahead time horizon. Modelling at this time horizon while accounting accurately for power system dynamics in PSSE
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Martin Australia invite applications for a project under this program, advancing robotic perception systems through monitoring of their machine learning models. Run-Time Monitoring of Machine Learning