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novel research methodologies in computer vision, deep learning architectures, and neuro-fuzzy systems to contribute to the development of robust AI frameworks for medical diagnosis and treatment support
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, Computer Science, Electronics Engineering or equivalent. Experience in one or more of the following areas: machine learning, deep learning, software-hardware co-design, computer system performance, design
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optimization of multi-modal LLMs. Investigate and implement methodologies to ensure AI authenticity, accountability, and the integrity of digital content. Develop and refine machine learning and deep learning
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for active learning. The role will work at the intersection machine learning, high-throughput computation, and inorganic crystalline materials discovery, focusing on accelerating the design and
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, electrical & electronic engineering, or equivalent. Background knowledge in signal representation/processing, visual data compression, and data-driven and machine learning/analysis. Prior research experience
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integration and AI models tailored for fish behaviour, health, and stress signal analysis. Investigate and apply novel machine learning and deep learning techniques for pattern recognition, classification, and
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into operational use cases. Prepare data collection frameworks and work on fish health monitoring datasets for machine learning training and benchmarking. Support the development of translational “lab-on-farm
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advances the mathematical foundations, algorithms, and real-world applications of epistemic uncertainty in machine learning, with a strong focus on imprecise probabilities, uncertainty representation and
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testing data Development of machine learning models for battery health assessment and remaining useful life prediction Job Requirements: PhD degree in Electrical Engineering or related subjects. Expert
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areas. Key Responsibilities: To independently undertake research in computer vision and machine learning. To produce research reports and/or publications as required by the funding body