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candidates must hold (or close to completing) a PhD in a relevant subject. Knowledge and experience in computer vision is required. Experience of efficient ML techniques, edge AI hardware platforms, low-power
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disease. This will involve data-driven feature extraction and the training and optimization of various machine learning models. The research will focus on analysing data from deeply phenotyped cohorts
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listeners. We are seeking candidates with a Ph.D. (either awarded or nearing completion) or equivalent professional qualification and experience in Machine Learning, Statistics, or a related field, who have
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term basis for 36 months due to funding restrictions. As part of your role, you will: Develop novel Bayesian machine learning approaches for psychoacoustic modelling. Publish your findings at top-tier
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Management Architecture – Platform for aircraft (machine learning based algorithms could be employed to process the data provided by such approaches). You will also be responsible for managing the research
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About the Role We are looking for a Research Fellow to join our NTU-GridLab team to work on advanced machine learning techniques in the area of energy systems with a specific focus on local energy
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), through the development and application of a digitalized Health Management Architecture – Platform for aircraft (machine learning based algorithms could be employed to process the data provided by
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analysis, medical image computing, and machine learning. While previous experience in these areas is advantageous, it is not essential. Familiarity with magnetic resonance imaging (MRI) is preferred
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analysis, medical image computing, and machine learning. While previous experience in these areas is advantageous, it is not essential. Familiarity with magnetic resonance imaging (MRI) is preferred
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machine learning for functional materials design. You should have a PhD in Computer Science, Chemistry, Physics, Materials Science, or a related discipline, with a commitment to developing collaborative