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Field
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tools. Processing of collected data; Implementation of Machine Learning; Development/implementation of the model prototype in the digital solution (app/platform) to support the clinical trial
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assessed according to the following weights and criteria: - Criterion 1: Absolute merit of curriculum vitae - Criterion 2: Academic performance in the areas of Machine Learning, Data and Information Fusion
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foods including through high moisture extrusion. Key responsibilities will include: Explore innovative methods for food process optimization including the use of AI and machine-learning Develop and
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coagulation factor proteins to investigate the mechanisms of coagulation and drug action. The experiments will include enzymatic, clotting, antigen and thrombin generation assays. Mouse models may be used
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topics such as statistics, high performance programming, machine learning and using data to constrain cosmological models. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs
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of at least some of the following: – Extensive independent research experience – Creativity and independence – Experience analyzing hyperspectral data and developing machine learning models - Genetic
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, governance, and community-engagement protocols, particularly for Indigenous and rural data Adapt AI and machine translation methods for low-resource and Indigenous languages Evaluate model fairness, bias, and
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 2 months ago
inference and/or machine-learning approaches. You will be able to articulate and prosecute an innovative interdisciplinary research vision, contribute new modelling or analytical methods, and integrate
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includes optimizing photonic enhancement techniques for improving the optical scattering of single impurity particles. In this regard, surface engineering methods, theoretical modeling and optical microscopy
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for machine learning and artificial intelligence, with a strong emphasis on developing and applying models such as LSTM and other time-series analyses to predict the longevity and behaviour of bioactive