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On-device machine learning (ML) is rapidly gaining popularity on mobile devices. Mobile developers can use on-device ML to enable ML features at users’ mobile devices, such as face recognition
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The project involves building and curating a comprehensive food image dataset suitable for mobile AI applications. High-accuracy deep learning models will be trained on this dataset and then
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This project focuses on developing algorithms capable of automatically identifying and categorizing mobile ringtones. This involves leveraging machine learning techniques to analyze audio signals
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Mobile and continuous health monitoring has seen major advancements in recent years. The capabilities of current mobile phones and their built-in sensors have inspired many mobile sensing
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, development and evolution of our digital platforms, including web applications, mobile apps and integrated services used by clinicians, dietitians, researchers and patients worldwide. This role offers a rare
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development (web/mobile) Knowledge of sustainability and behavioural nutrition science
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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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, identifying molecular disease signatures and matching them with the most effective therapeutic interventions are essential. The Hudson‐Monash Paediatric Precision Medicine (HMPPM) Program aims to develop and
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campus. This Level A research-only position will contribute to the research program of the Signalling Network Laboratory, undertaking projects focused on specific members of the protein kinase superfamily
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brain health. We are seeking a highly motivated Research Fellow to join a multidisciplinary and multi-institutional research program within the CDCO Academic Node. This role focuses on evaluating novel