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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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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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framework to identify and prioritise components suitable for metal additive manufacturing. Applying AI, data analytics and machine learning to evaluate 2D and 3D design data. Collaborating with defence
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 12 days ago
scientists, including experts in machine learning and Al. The School Research School of Chemistry (RSC) strives for excellence in research and training in the areas of Biological Chemistry, Materials Chemistry
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, rotating machines, and energy storage/load management systems. 3. Control Systems Focus: You will design and implement advanced control strategies for wind turbine operation, contributing expertise in
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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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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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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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an opportunity for a Postdoctoral Fellow. You will contribute to UNSW’s research efforts in developing machine learning algorithm for photovoltaic applications and utilising them for the investigation
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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