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Field
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drugs at the target site, using only an internal battery and on-board sensors for fully autonomous operation. The overarching goal is to develop a battery-powered ingestible capsule that autonomously
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an adaptable Machine Learning (ML) hardware architecture to solve Artificial Intelligence (AI) classification tasks using Internet of Things (IoT) sensor data. This will be a small system-on-chip designed
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project’s focus is to: Conduct cutting-edge experiments to investigate how surface texture affects seal performance and explore the use of an ultrasonic sensor for real-time monitoring. Experiment with
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the Internet of Things (IoT), where networked sensors and actuators enable real-time adaptation to environmental changes. Consider a self-adaptive IoT network such as a smart home that autonomously manages
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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tissue, radiologists insert a localisation device (RFID, magnetic seed) into the tumour pre-operatively. Wireless methods hold promise however, due to the scale of current RFID tags, delivery is
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rigidity to penetrate without damaging tissue and stability against peristalsis. The device will also include onboard electronics for wireless data transfer to an external receiver. The device will be tested
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operating filters. Quantify operational performance including headloss recovery, filtrate turbidity, biological stability and lifecycle carbon—using high-resolution sensor data and life-cycle assessment tools
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focuses on the analysis of neuronal networks in the Drosophila brain, and the Hummel team currently consists of postdocs, pre-docs, master students and administrative colleagues who share a common interest
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, curriculum over geometry/BCs, calibration of predictive uncertainty, and robustness to sensor noise. Tasks: forward prediction (temperature fields), inverse reconstruction (defect size, depth, orientation