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Field
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developing intelligent algorithms that can support repair and remanufacturing decisions for sustainable manufacturing? As a PhD researcher, you will create innovative machine learning solutions to optimize
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based on ultrasound sensors. This is a critical advancement as the aging population increasingly demands better healthcare solutions to chronic diseases. Proper monitoring has the potential to improve
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are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we
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imaging systems capable of penetrating fog, dust, and even certain solid materials. These systems will deliver detailed, high-resolution imaging in challenging conditions where conventional optical sensors
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, quantum networks are slowly becoming a reality. The coordination algorithms that govern their operation are unlike those employed in classical networks, necessitating novel verification approaches. The goal
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completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation
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based on ultrasound sensors. This is a critical advancement as the aging population increasingly demands better healthcare solutions to chronic diseases. Proper monitoring has the potential to improve
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reference architecture for data visiting. This paradigm enables algorithms to securely access and process data within the environments where it resides, supporting federated learning for training machine
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experimental testing. You’ll design and run experiments, write and train algorithms, and contribute to open-source tools that may one day become industry standards. This project offers the freedom to explore
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limitations. The field of interpretable machine learning aims to fill this gap by developing interpretable models and algorithms for learning from data. Meanwhile, the field of knowledge discovery and data