4,055 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL"-"UCL" positions in United Kingdom
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, microbial cultures, and cleaning validation samples. Develop data analysis pipelines for Raman spectral classification, potentially integrating machine learning methods. Research & Project Responsibilities
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languages and environments. Experience with machine learning and AI in biomedical settings would be considered favourable. You will also need a problem-solving ability to manage and analyse datasets and the
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experience with digital technologies such as cloud platforms, data analytics tools, automation tools, or AI/ML frameworks. - Significant experience applying advanced analytics (e.g., machine learning, NLP
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using liquid biopsy next generation sequencing data for cancer diagnostics. About You Must have a strong background in next generation sequencing data analysis/machine learning, cancer and/or genome
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Associate will work on projects coordinated between both experimental chemistry and machine learning academics. The project will be computational in nature, but the post-holder will work together
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expertise in programming (C++, Python), computer architectures and Deep Learning (PyTorch, TensorFlow). Exceptional international candidates may be eligible for a fee waiver (read the following section
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The University of London The University of London is both the UK’s largest provider of international distance and online learning and the convenor of a federation of 17 renowned higher education
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International Business. We seek colleagues who are passionate about education, committed to student learning, and keen to contribute to the future of teaching in these fields. The School of Management is one
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using liquid biopsy next generation sequencing data for cancer diagnostics. About You Must have a strong background in next generation sequencing data analysis/machine learning, cancer and/or genome
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with expertise in areas such as Machine Learning and Deep Learning, Generative AI, Data-Centric and Responsible AI, or AI for real-world applications (e.g., health, cybersecurity, robotics, or business