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a role model and fostering an inclusive working culture. Person Specification PhD, or close to completion, in a relevant, quantitative field, e.g. meteorology, machine learning, climate science
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artificial intelligence methodologies. The successful candidate will work at the forefront of computational biology, developing novel approaches for large-scale genomic data analysis and contributing
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neurocritical care research The Opportunity We are seeking a Research Fellow - Data Science professional with strong expertise in machine learning, deep learning and high-frequency physiological signal analysis
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of machine learning techniques Proficiency using data science tools (e.g., Python, R, MATLAB, MySQL, Jupyter Notebooks) Excellent verbal and written communication skills including the ability to communicate
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illness. We have a large team working on developing technological solutions for these applications. We are seeking a computer science researcher to take an active role in developing novel machine learning
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technologies, grid computing and physics data analysis, machine learning, and interactive and collaborative systems. The prospective PhD candidate will work in close cooperation with our current PhD students
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decision tool based on SLP/NLP, and utilizes large language models. The project will focus on interaction with clinicians, with a goal of closing the gap between foundational research in machine learning and
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postdoctoral Research Fellow (RF) position for one year with a possible extension for one more year. The starting date is November or December 2025. This post will advance the application of Machine Learning (ML
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experience in spatial analysis and/or machine learning methods, and an interest in applying these tools to urban and housing policy questions. The Fellow should demonstrate potential for producing high-quality
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Desirable criteria Experience of advanced statistical and/or machine learning methods, such as longitudinal analysis methods, latent variables models, clustering algorithms, missing data and clinical trial