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and MALDI/ESI, and lignin composition using TDA/GCMS. The postdoc will work closely with teams developing engineered plants to develop a deeper understanding of cell wall architecture. This is expected
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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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Australian National University | Canberra, Australian Capital Territory | Australia | about 4 hours ago
Classification: Academic Level B Salary package: $118,632 - $134,507 per annum plus 17% superannuation Terms: Full time, Fixed term (up to 3 years) The Position This postdoc will explore
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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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strong sense of community & inclusion Enjoy a career that makes a difference by collaborating & learning from the best At UNSW, we pride ourselves on being a workplace where the best people come to do
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 18 hours ago
&PEWER_Academic B_Research Fellow.pdf The Position This postdoc will explore the geochemical behaviour of critical metals in the Earth’s crust as a function of variables such as composition, temperature, pressure
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Control” (DP250101137). The Postdoctoral Research Fellow is expected to undertake research on control theory and machine learning techniques for project DP250101137. The Postdoctoral Research Fellow will
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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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contribution of 17% superannuation applies. Fixed term position for 24 Months. Postdoc opportunity - Open for Applications Until Filled. We are seeking a dynamic and motivated Postdoctoral Fellow in Ecological
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Simulation group to apply classical Molecular Dynamics and Machine Learning approaches for development of a new class of hybrid polyphenol-lipid nanoparticles with tuneable internal structure and exploration