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Current federated learning architectures in mobile healthcare are limited to a centralised model without considering the full continuum of mobile-edge-cloud. Additionally, to support different data
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, minimizing energy costs and environmental impact. This offers opportunities to work on practical applications in sustainability for cloud and edge systems. Privacy-Enhancing Resource Management: Example: A
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analysis, or multi-omics integration, with strong competence in deep learning frameworks (e.g., PyTorch/TensorFlow) and data engineering for reproducible research. Familiarity with cloud/HPC workflows
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and maintenance of information systems and digital services using a wide variety of tools and platforms, including but not limited to web, mobile, conversational AI and cloud infrastructure. This is a
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To be successful in the position, you would need to have: Proven leadership in team management and capability development Deep expertise in VMware, Linux and cloud infrastructure Proficient in systems
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to cloud-based machine learning services, on-device ML is privacy-friendly, of low latency, and can work offline. User data will remain at the mobile device for ML inference. Problems: In order to enable
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latency, increase throughput, and enable real-time resource management, preparing them for impactful roles in AI, cloud computing, and large-scale system design. A practical example of this project includes
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intelligent techniques for scheduling and offloading tasks to the cloud and peer vehicles. This will ultimately meet the Quality of Service (QoS) requirements of time-critical road safety applications and