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/6GIC), is looking to recruit a Research Fellow in applied adversarial machine learning. This exciting opportunity will contribute to the D-XPERT (AI-Based Recommender System for Smart Energy Saving
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machine learning, bioinformatics, sequencing and microbiology. This role is available on a fixed term basis until 31st May 2027. Hours of work are full time (36.25 hours). Job share arrangements may be
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Fellow in applied adversarial machine learning. This exciting opportunity will contribute to the D-XPERT (AI-Based Recommender System for Smart Energy Saving) project. The D-XPERT project represents a
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the power of machine learning, you will explore the interplay between metabolomic and proteomic data and depression to transform our understanding of this pervasive disorder. Based at King’s College London, a
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the power of machine learning, you will explore the interplay between metabolomic and proteomic data and depression to transform our understanding of this pervasive disorder. Based at King’s College London, a
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computer vision is required. Experience of efficient ML techniques, edge AI hardware platforms, low-power computing, earth observation is desirable. They will have excellent programming skills (Python, C
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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