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management and analysis of EHRs, a background in data science and machine/deep learning, and solid programming skills in Python and/or R. The program has already collected large clinical data sets-we are now
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making sure these are executed in a timely fashion and to deadlines. • Be willing to learn and develop new computational and wet-lab techniques and approaches required for the project as it evolves
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certification in data science, machine learning, or analytics, expertise in immunohistochemistry and digital imaging techniques. Previous experience working in a molecular or biochemistry laboratory and/or prior
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. The Department seeks candidates with interests in Statistical research at the interface of machine learning and AI. They will have the skills and enthusiasm to lecture graduate level, over a wide range of topics
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challenge. We seek a senior computational biologist to apply these extensive in-house datasets toward the development of novel, domain-tailored machine-learning models and analytical methods. You will explore
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Engineering, Mathematics, Statistics, Computer Science or conjugate subject; strong record of publication in the relevant literature; good knowledge of machine learning algorithms and/or statistical methods
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highly computer literate, with demonstrable experience in using common software applications e.g. MS Word, Excel, Outlook, Adobe Acrobat, and have the ability to learn quickly and use specialist software
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computer vision software for quantitative microscopy, Deep Learning for Time-lapse Analysis (DeLTA). You will be responsible for implementing a graphical user interface, extend the software with real-time
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, learning and decision making, you will have strong quantitative and programming skills along with a track record of designing neuromodulation and neuroimaging studies in healthy participants, of using
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discipline (eg Statistics, Machine Learning, Biostatistics, AI, Engineering) with experience of developing and applying new methods. You will be able to develop research projects, with publications in peer