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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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collaboratively within an interdisciplinary research environment. Desirable experience with advanced AI or machine-learning methods beyond standard predictive modelling prior exposure to qualitative or mixed
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processes. Your responsibilities will include: Conducting high-quality research on the suitability of available methods to model metal-ligand complexes in water, with a focus on machine learning techniques
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‑science and machine‑learning techniques to improve the performance and reliability of existing models, including classification and prioritisation models. Develop, test, and refine analytical approaches and
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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North America, to improve an existing model for the spread of Cyvirus cyprinidallo3 (also known as Cyprinid Herpes Virus or CyHV-3) as a biocontrol agent for common carp in Australia. The modelling will
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North America, to improve an existing model for the spread of Cyvirus cyprinidallo3 (also known as Cyprinid Herpes Virus or CyHV-3) as a biocontrol agent for common carp in Australia. The modelling will
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North America, to improve an existing model for the spread of Cyvirus cyprinidallo3 (also known as Cyprinid Herpes Virus or CyHV-3) as a biocontrol agent for common carp in Australia. The modelling will
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lung health, across a range of laboratory and imaging programs. Role Summary This role will help to develop lung function-based respiratory biomarkers through AI, machine learning and image processing
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machines, system integration and electrical reticulation/protection. This role will also see you work collaboratively with a multidisciplinary team to advance renewable energy technology through cutting-edge