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Classification Title: OPS Machine Learning Assistant Job Description: The OPS research assistant will support multiple ongoing research initiatives within the laboratory, contributing
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for extracting physiological biomarkers from ECG, PPG, and related sensor data Machine learning and AI for predictive modelling and risk stratification Computational physiology modelling to personalise and
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network models on computing clusters Knowledge, Skills and Abilities: Demonstrated fluency in Python, and willingness to keep up with new machine learning libraries (pytorch, lightning, …) Knowledge
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networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity rules which enable effective learning in large and deep networks and is consistent with
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
degree of independence and commitment Experience with machine learning and high-performance computing is advantageous, but not necessary Our Offer: We work on the very latest issues that impact our society
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successful courses or projects) Be proficient in programming (preferably in Python ot Matlab). Ideally familiar with machine/deep learning, signal processing, dynamical system or mathematical modelling To find
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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in machine learning and/or computer security and Experience working with LLMs or agent-based systems. Informal enquiries may be addressed to adel.bibi@eng.ox.ac.uk For more information about working at
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the communication and presentation of scientific data. The intern will gain new skills in machine learning pipelines, model training and data management. Job Duties Attends weekly lab meetings. Works with trainees
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atmospheric chemistry, air quality, and model development. Basic Qualifications: Ph.D. in atmospheric science and substantial experience in modeling of atmospheric composition. Additional Qualifications