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machine learning methods to the analysis of large-scale astronomical datasets, with a particular emphasis on time-domain astronomy. Research directions will be flexible and shaped according to mutual
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formula. Preferred Qualifications Familiarity with Python, R and MATLAB. Experience with machine learning/deep learning. Master’s degree in a related field. PhD or near to completion of a PhD (or equivalent
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and curate LC-MS/MS data for high-quality feature extraction Design and train machine-learning models for mass spectrometry and chemometric data Integrate multi-omic data including genomics and
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the Safety and Surveillance Group and the wider Agency. The successful candidate will have: A PhD in Computer Science, Statistics, Machine Learning, Data Science or a similar quantitative discipline or
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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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About the Role We are seeking an enthusiastic and motivated postdoctoral researcher to apply advanced data analytics and machine learning techniques to real-world clinical data in the field of viral
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expertise in analysing/ training models on biological or chemical datasets Proficiency in Python for data science and machine learning Possess sufficient breadth or depth of specialist knowledge with deep
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researcher in natural language processing and large language models to work with a team from multiple disciplines of machine learning and artificial intelligence to develop multimodal large language models
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implementing signal processing algorithms specifically tailored to analyze signals that contain interfering impulsive content, often encountered in data coming from main and pitch bearings. Machine learning
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recent large-scale capabilities in physics. Reliability, exploring uncertainty quantification and robust inference in machine learning. Explainability, leveraging identifiability and unique recovery