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: This 4 year fully funded studentship is open to applicants with a first-class or upper second-class degree (or equivalent) in Electrical Engineering, Machine Learning, Physics, Data Analytics or other
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Python and C++ with relevant computer vision, signal processing, machine learning and/or deep learning tools (TensorFlow, PyTorch, Keras, OpenCV etc.). A track record of publishing academic papers, open
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this challenge head on by combining quantum-mechanical calculations with state-of-the-art machine learning (ML) methodologies to explore and optimise the compositional space of complex high-entropy metal oxides
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for this vacancy they may be eligible for a bonus. Please ensure when applying, that you select ‘other’ and note their name when answering the question ‘Where did you learn of this job?'. The referrer must be noted
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: Experience in software development in topics such as computer vision, audio signal processing, machine learning, deep learning, and/or sensor systems. Experience in collaboration and technology transfer
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., statistical modelling, Artificial Intelligence/Machine Learning) Experience in computational biology and bioinformatic analysis Proven ability to write detailed, technically accurate reports on complex research
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viewing conditions and across languages. By combining behavioural methods with machine learning, it aims to develop physiologically-based models of lexical colour categorisation. This is a multidisciplinary
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willing to learn new applications. Benefits Membership of the OSPS pension scheme, free medical insurance (taxable benefit), 30 days annual leave (plus bank holidays), free meals on duty, use of on-site gym
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wide range of libraries/frameworks and cloud infrastructure tools. Familiarity with Machine Learning, Neural Network and AI driven image analysis methods Experience with data management for large data
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Traditional machine learning (ML) approaches require large volumes of annotated defect data and significant manual oversight. For the aerospace industry, this, combined with rare, but critical, defect examples