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or more of the following areas: natural language processing, multimodal learning, large language models, knowledge graphs, graph learning or data mining; (c) be proficient in Python and deep learning
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changes required for heterogeneous CAPTURE-PH imaging. Develop and optimise supervised deep-learning methods for segmentation, feature extraction, classification and prediction, including retraining or fine
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and Computing Sciences, and the Plant-Microbe Interactions group external link in the Department of Biology. The research combines machine and deep learning (AI), microbiome biology, microbial genomics
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intelligence, computer science, remote sensing, geomatics, data science, or a forest/environmental science discipline with a strong quantitative or AI component Strong knowledge of machine learning and deep
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Engineering, or related field. At least 3 years of relevant experience in computer vision, artificial intelligence, etc. Proficiency in programming languages such as C and Python Proficiency in deep learning
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multimodal sensor data (video, LiDAR, BIM). • Assist in the deployment and acceleration of deep learning models (e.g., TensorRT, ONNX) onto edge computing devices on the robot. • Develop interactive web
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degree in Computer Science, Artificial Intelligence, Robotics, Electrical Engineering, or a closely related field. A strong academic record and solid background in machine learning and deep learning
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flexible fitting of mechanistic and statistical models to the data. Our current focus is on generating datasets of sufficient size and diversity to train machine learning models to accurately predict how
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Intelligence, Machine Learning, Natural Language Processing, or a closely related discipline. Demonstrated expertise in Large Language Models (LLMs), Natural Language Processing, Efficient Deep Learning, AI
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discipline. Strong background in machine learning, deep learning, and large language models. Experience working with large-scale datasets and distributed model training. Strong programming skills in Python