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sustainable resource extraction for geothermal heating. The outcomes will support improved prediction, design, and performance assessment of geothermal systems, contributing to risk reduction and more efficient
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addressing sustainable resource extraction for geothermal heating. The outcomes will support improved prediction, design, and performance assessment of geothermal systems, contributing to risk reduction and
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science or systems engineering. Knowledge of AI/ML algorithms, particularly graph neural networks and reinforcement learning, is highly advantageous. A keen interest in distributed computing, IoT architecture, and
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key challenge lies in enabling agents to continuously adapt their knowledge and behaviour in dynamic environments while keeping decision-making processes explainable. This motivates the use
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, an interest in biomedical research, and the ability to work independently are essential. Prior experience/knowledge in Electromagnetism, COMSOL, microfabrication, and PCB design are desirable but not essential
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Skills/Background The applicant should have a solid background in computer science or systems engineering. Knowledge of AI/ML algorithms and simulation environments is highly advantageous. A keen interest
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, trustworthy AI for the real-world applications, gaining hands-on experience with the latest AI technologies, contributing to knowledge transfer, and receiving opportunities to develop leadership, and research