Representation Learning
Representation Learning
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Justus Piater, Antonio Rodríguez-Sánchez, Samuele Tosatto
This is a university doctoral position that involves minor teaching duties. The precise research topics are negotiable within the scope of active research at IIS, including machine learning and growing levels of AI for computer vision and robotics. Of particular interest are topics in representation learning and causality for out-of-distribution situations.
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The position integrates into an attractive environment of existing activities in artificial intelligence such as machine learning for robotics and computer vision, natural language processing, recommender systems, schedulers, virtual and augmented reality, and digital forensics. The candidate should engage in research and teaching in the general area of artificial intelligence. Examples of possible foci include machine learning for pattern recognition, prediction and decision making, data-driven, adaptive, learning and self-optimizing systems, explainable and transparent AI, representation learning; generative models, neuro-symbolic AI, causality, distributed/decentralized learning, environmentally-friendly, sustainable, data-efficient, privacy-preserving AI, neuromorphic computing and hardware aspects, knowledge representations, reasoning, ontologies. Cooperations with research groups at the Department of Computer Science, the Research Areas and in particular the Digital Science Center of the University as well as with business, industry and international research institutions are expected. The candidate should reinforce or complement existing strengths of the Department of Computer Science.
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