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modelling, wavefront shaping, and machine learning algorithms. Information and application Are you interested in this position? Please send your application via the 'Apply now' button below before 15 June
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Recognition Machine Learning and Pattern Recognition are subareas of AI aimed at the development of algorithms and models capable of learning from data, recognizing patterns, and signal analysis. Tasks include
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at the development of algorithms and models capable of learning from data, recognizing patterns, and signal analysis. Tasks include image and speech recognition, recommendation systems, and predictive analytics
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through co-creation events and expert consultation. Collect and analyze data from public sources and literature to monitor allergen risks in novel foods. Develop and implement AI algorithms for identifying
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algorithms for multi-parameter estimation. The Collaborative Ecosystem: You will be a Post-Doc embedded in the Multi-Modality Medical Imaging group within the strong photoacoustic-ultrasound community
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of –omics data. The group has a strong track record in (integrative) computational omics analysis, algorithm development, machine learning and scientific data infrastructure. There are many national and
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. Your work will focus on design-space exploration and optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g., term rewriting), to algorithmic
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, together with relevant expertise in areas related to Artificial Intelligence, such as: Foundational Models, Algorithmic Research Machine/Deep Learning Computer Vision Parallel & Distributed Computing Control
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consumption, create energy labels for algorithm scalability, and guide implementers in choosing more efficient algorithms. Ready to make AI more sustainable? Apply now! The goal of your PhD project is to
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will also collaborate with project partners in other European institutions. Our team has developed and deployed AI algorithms for recognising the sounds and images of Europe’s wildlife. In this project