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research partnerships with industry, boosting R&D investments leading to economic growth, and attracting highly qualified talent. SnT invites applications from PhD candidates in the general area of machine
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, the university hospital of TUM. Our offer We are currently seeking two PhD candidates to join our international team of scientists from the fields of machine learning, computer vision and medical imaging. We offer
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aureus (Orazi et al. mBio 2019). You will apply state-of-the-art, machine learning methods (deep neural networks and evolutionary algorithms) on big-data from thousands of individual bacterial (Lapinska et
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extremist networks, the project will use advances in analytics and machine learning to model and reveal effective intelligence targeting and disruption strategies. For more information, please see https
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duties at the Department). About the project/work tasks Causality is a fast-developing area of machine learning, holding the promise to improve artificial reasoning and intelligence. Causal models can
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transposable elements (TEs), major component of most plant genomes especially those large genomes. Deep-learning models (currently used for computer vision and natural language processing), which can encode high
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, more agile solutions. Current machine learning (ML) algorithms identify and predict threats but rely heavily on past datasets, requiring significant updates. Continual learning offers a solution by
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competence in artificial intelligence, machine learning, machine vision and robotics, as well as Biometrics (statistics and mathematics with applications in biological systems). Read more about our benefits
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Moments (NGMoM) model that efficiently models gas-gas collision and gas-solid scattering kernels. This model will incorporate detailed molecular interactions using machine learning algorithms to infer
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using (statistical) machine learning techniques, and like all such systems their operations are opaque. In other words, it is not clear - even to the designers of these systems - how exactly they process