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which to develop, integrate, and stress-test new technologies under realistic operating conditions. In parallel, SIT works closely with the maritime ecosystem on decarbonisation and digitalisation
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in food manufacturing - smart sensors, digital twins, and AI-driven food quality control and processing; Food safety - supply chain tracking/monitoring, blockchain Biotechnology: Host strain
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- smart sensors, digital twins, and AI-driven food quality control and processing; Food safety - supply chain tracking/monitoring, blockchain Biotechnology: Host strain screening and engineering for
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that provide a consistent, spoilage-free feedstock. Deliver a reproducible, batch-to-batch-consistent process suitable for multiple downstream users. Evaluate processing options to improve yield
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formulation and ingredient functionality Product quality and sensory evaluation Food processing and manufacturability Nutrition and healthier reformulation Sustainable and emerging food systems
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processing and machine learning. Working knowledge of computer vision and deep learning concepts, including object detection and image-based classification, with hands-on experience using Python and at
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more of the following areas: Computer Vision and Image Processing Machine Learning, Deep Learning, and Reinforcement Learning Large Language Models (LLMs) and Multimodal Models Generative AI, Agentic AI
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and Image Processing Machine Learning, Deep Learning, and Reinforcement Learning Large Language Models (LLMs) and Multimodal Models Generative AI, Agentic AI, Physical AI, and Embodied AI Trustworthy AI
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competitions, showcasing practical problem-solving and model development skills Model deployment (e.g., ONNX, TensorRT) Edge computing or embedded vision systems (e.g., NVIDIA Jetson Nano) Real-time processing
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testing, and data-driven engineering evaluation. Familiarity with material treatment or processing methods (e.g. stabilisation, carbonation, or related technologies) will be advantageous. Experience in