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required. • Programming skills are required. • Knowledge of Natural Language Processing and Machine Learning is preferred. • Fluent English required, both oral and written. French is appreciated but
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applicant: has a PhD degree in electrical, computer or biomedical engineering, computer science, data mining/machine learning, or a closely related area. has demonstrated the ability to perform independent
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machine learning, deep learning for medical imaging, generative AI and more. The positions are open to both those who are specialized in methodological development, as well as those focused on innovative
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of statistics, bioinformatics, and/or machine learning approaches are desirable but not required. This is a permanent position within the Nature Portfolio. The successful applicant will primarily support Nature
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at the Assistant, Associate or Professor level. We are currently recruiting candidates with expertise in data science, machine learning, computational or systems biology, and/or bioinformatics, with interest in
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, machine-learning, and protein design to develop novel transposon-based genome-editing tools. Located on the 6th floor of the new Inspiration4 Advanced Research Center (opened in 2021), the Kellogg lab leads
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geological field-based methods and big data applications and machine learning methods. Research focus will be on feedback processes between erosion, sedimentation, tectonics and climate, and topics could
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machine learning models for the diagnosis of temporomandibular disorders (TMD) based on jaw motion time series data. Moreover, the successful candidate will be affiliated with the Comprehensive Center AI in
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NOVA Institute for Medical Systems Biology (NIMSB) announces Four Independent Group Leader positions
for integration of large-scale omics datasets, and application of machine learning and statistical modelling for decipher cell and tissue behaviour, elucidate disease mechanisms, and enable patient stratification
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utilizes a widely available diffraction-limited spinning disc confocal microscope (although not limited to this modality) for imaging. A single-step, machine-learning based approach is then applied