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well as an active engagement in the 3RTG activities. Requirements: successfully completed university degree (Master's, Diploma or equivalent) and relevant PhD in Computer Science, Computer Engineering, or related
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• US or EU patents in the field of machine learning / computer vision We Offer • Remuneration according to TV-L E14 (100%) • Fixed-term position for the project duration (48 months) • Central role in a
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Science, Electrical/Computer Engineering, or a related field by the start date, with a strong publication record in computer vision, multimodal learning, or vision–language models. We require hands-on expertise with
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. The post is offered at a competitive salary (Grade 6, Spine Point 33 on the KCL salary scale), and includes provisions for travel money, computer equipment and academic and leadership training. This is a
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the Centre for IoT and Telecommunications (CIoTT) in the School of Electrical and Computer Engineering. CIoTT is internationally recognised for excellence in wireless communications and signal processing and
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/ ) – an European network of excellence in AI, Machine Learning (ML) and Computer Vision (CV), of which Vittorio is a Fellow member. For this particular position, the focus is on investigating AI approaches
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at the Department of Electrical and Computer Engineering, Aarhus University, where we are advancing communication-efficient and distributed foundation model inference across the computing continuum
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for the candidate The candidate will not only deepen and enrich their expertise in AI and computer vision, but also become familiar with using AI to handle curvilinear features which are ubiquitous in many domains
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training and a significant track record in one of the following areas: -computational biology -computer science -electrical or computer engineering -genomics -neuroscience -population genetics / genomics
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communication, computer, and presentation skills. Ability to multi task and work cooperatively with others. Preferred Knowledge, Skills and Abilities: -Strong publication record in relevant scientific journals