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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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, business school scientists, system modeling and optimization researchers, computer scientists, legal experts and social scientists working on energy topics. Description of the PhD project The project
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described below? Are you our future colleague? Apply now! Education · A PhD in machine learning, AI, with a focus on application of AI on energy systems. Experience and skills · Strong
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calculated using our Software Energy Lab, which has multiple test machines with GPUs and, in the future, AI accelerators. Development teams currently lack guidance on how to create sustainable systems. You
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analysing large-scale datasets such as StatsBomb, which provide detailed technical and tactical data across multiple leagues and seasons. By applying advanced analytical and machine learning techniques
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biology. The applicant should also have an interest in learning, or previous experience in, computer programming, particularly using languages such as Python. The ideal candidate is driven and a creative
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Statistics for the Australian Grains Industry 3 (SAGI3). Investment. The University of Adelaide, in collaboration with Curtin University and The University of Queensland, is leveraging machine learning, data
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Supervisors: Prof. Gabriele Sosso, Dr Lukasz Figiel, Prof. James Kermode Project Partner: AWE-NST This project utilises advancing machine learning techniques for simulating gas transport in
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or machine learning frameworks Good oral and written presentation skills in a Scandinavian language at level A2 or higher Personal characteristics To complete a doctoral degree (PhD), it is important that you
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The molecular biosciences are undergoing a major paradigm shift – away from analysing individual genes and proteins to studying large molecular machines and cellular pathways, with the ultimate goal