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to develop AI models for image reconstruction from data from our ultra-thin fibre-based spatial frequency domain imaging device (SFDI) and also from our custom-built photoplethysmography (PPG) sensor
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(SFDI) and also from our custom-built photoplethysmography (PPG) sensor. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in
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Systems, or a related field. Strong analytical and critical thinking skills. Strong machine learning (ML), computer vision (CV), large language models (LLM) for quantitative data, texts, images, and sensor
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experience with maritime systems, radar/sensor fusion, or industry engagement is desirable. What we offer For information about our rewards and benefits please visit https://www.ucl.ac.uk/work-at-ucl/reward
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phenomena in atomically precise materials characterized using scanning tunneling microscopy and non-contact atomic force microscopy in a wide array of systems such as heterostructures of two-dimensional van
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, regression models, multistate models, simulation models, life table and decomposition approaches, causal inference, matrix population models). Desirable: B1. Scottish Credit and Qualification Framework level
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will generate a wide range of contemporary high-resolution next-generation sequencing and array-based genomic and epigenomic datasets across large cohorts of human tumours and experimental models
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impactful ways to empower – and invest in – our people. Our vast array of professional development opportunities means you will grow and learn, having the chance to participate actively in staff trainings and
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, Skills and Experience • You will have substantial technical experience in time series analysis, ideally either in neurophysiology data or wearable sensor data • You will have experience of at least
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contemporary high-resolution next-generation sequencing and array-based genomic and epigenomic datasets across large cohorts of human tumours and experimental models, alongside complex drug screening, efficacy