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This is a unified application form for all positions in the Beyesian Deep Learning group at KAUST led by Prof Maurizio Filippone, including Research Intern MS/PhD Student PhD Student Postdoctoral
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project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict
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As a postdoctoral researcher, your primary responsibilities will be: Develop machine learning and deep learning models, with a strong focus on computer vision, for the characterisation and
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, ParkinsonNL, ReumaNederland). In total the program will encompass 9 PhDs and 5 postdocs to address the issue of translating health care to the home environment. This PhD position is hosted by the Biomechanical
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methods involving molecular surface display and deep sequencing to study force-dependent behavior in protein systems. These datasets will, in turn, be used to train machine learning models capable
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
but are not limited to: development of new AI architectures for biology and hybrid models that combine deep learning with mechanistic models; foundation models of genome regulation using single-cell and
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hours: 40 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Reference no.: 6142 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique balance
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: 40.00 | Classification CBA: §48 VwGr. B1 lit. b (postdoc) Limited contract until: Job ID: 6142 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique
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| Working hours: 40.00 | Classification CBA: §48 VwGr. B1 lit. b (postdoc) Limited contract until: Job ID: 6142 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a
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Łukasiewicz Research Network – PORT Polish Center for Technology Development | Poland | 2 months ago
. The candidate will join ongoing projects in our team and perform mathematical/computational modeling. The topics include (1) constructing deep learning-based models to predict the molecular microenvironments