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Field
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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has gone through a very rapid development in the last few years. Large-scale machine learning models are however notoriously over-confident. With insufficient amounts of data to train them on together
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difficult to deploy outside large data centres. This PhD project focuses on developing resource-efficient computer vision methods that maintain strong performance while dramatically reducing computation
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part of an international team Desired Qualifications Familiarity with cloud computing platforms and large-scale data processing German language skills Publications in computer vision or machine learning
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CeMM - Research Center for Molecular Medicine of the Austrian Academy of Sciences | Austria | about 22 hours ago
the Medical University of Vienna, the Technical University of Vienna and University of Vienna, the AITHYRA and CeMM PhD programs for both life scientists and computational scientists/machine learning experts
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from reactive to proactive. The goal is to increase transparency and trust in the DNS namespace. Key research activities will include applying machine learning and graph-based techniques to uncover
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to 1) Object-attribute compositionality to replace exhaustive data requirements with structured concept learning, 2) Bias detection and machine unlearning to identify and mitigate bias and shortcuts
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The AITHYRA-CeMM Joint International PhD Call in Molecular Medicine and Artificial Intelligence (m/f
for both life scientists and computational scientists/machine learning experts will start in September 2026. We offer: a highly interactive introductory program in a supportive peer-group environment
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PhD Research Fellow in Multimodal Large Language Models for Patient-Generated Data in Shared Decision Making (DC7) (ENDOTRAIN) PhD position at Adhera Health SL (Spain) and the University of Bergen
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. The research in the PhD project will focus on core spatio-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation