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solid experience in programming, particularly in Python and JavaScript. Significant experience in data science and machine learning will be highly valued. You like to work in a team while demonstrating
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execution. This work involves creating frameworks for adaptive decision-making, using techniques from operations research and machine learning. This particular thematic area will be supervised by Associate
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of their research projects, fellows in the Smith lab have the opportunity to work one on one with the PhD biostatistician associated with our laboratory to learn introductory machine learning methodology such as
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modeling, artificial intelligence (AI), machine learning, and neuroinformatics methods to address pressing questions in clinical neurology. This position offers opportunities to work on large biomedical
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difficulties in the research projects. It is essential that you hold a PhD/DPhil in a quantitative discipline (eg Operations Research, Management Science, Statistics, Machine Learning, Applied Mathematics
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vehicle validation. Design and implement machine learning models that capture the complexity and variability of real-world traffic situations, including unusual pedestrian behaviors, edge cases, and multi
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in computational science, machine learning, and experience with synchrotron data analysis are strongly encouraged to apply. Position Requirements PhD completed in the past 5 years or soon to be
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obtainable using the Oxford Nanopore sequencing platform and improve genome recovery from metagenomes by developing new binning algorithms based on machine learning. This postdoc position will utilize
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the Oxford Nanopore sequencing platform and improve genome recovery from metagenomes by developing new binning algorithms based on machine learning. The postdoc will be part of the Microbial Metagenomics group
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start quickly and effectively, leveraging your experience in data analysis, machine learning and biomarkers quantification to contribute from the onset. You will liaise with external collaborators and