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Interview Date: To be confirmed Reference: CSS-0224-25 We are looking for enthusiastic individuals to apply for a two year fixed term fellowship in Cardiothoracic Surgery, in the Department of Clinical
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of computer vision and machine learning. Previous experience of real time systems development in Python, OpenCV, PyTorch and deep learning are essential. Experience of C/C++/C#, TensorFlow would be beneficial
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and programming skills and experience of computer vision and machine learning. Previous experience of real time systems development in Python, OpenCV, PyTorch and deep learning are essential. Experience
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of computer vision and machine learning. Previous experience of real time systems development in Python, OpenCV, PyTorch and deep learning are essential. Experience of C/C++/C#, TensorFlow would be beneficial
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months, in the first instance. Application Process If you would like to discuss this role please contact Prof Regent Lee at regent.lee@nds.ox.ac.uk Applications for this vacancy are to be made online. You
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to contribute to an exciting European project developing hydrothermal processes for the management of solid digestate and other waste from the olive oil production process. About the Role We are seeking a
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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the Medical Research Council. The Research Fellow will be using Natural Language Processing (NLP) methods, with a special focus on generative Large Language Models (LLMs), to interrogate a very large sample of
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Language Processing (NLP) methods, with a special focus on generative Large Language Models (LLMs), to interrogate a very large sample of Electronic Health Records from people with epilepsy across multiple NHS
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the Medical Research Council. The Research Fellow will be using Natural Language Processing (NLP) methods, with a special focus on generative Large Language Models (LLMs), to interrogate a very large sample of