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Field
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implement algorithms for image processing and analysis of multisensor data (RGB, LiDAR, hyperspectral). Design and implement explainable AI (XAI) models for the detection and quantification of grape berry
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reliability of robotic systems. In this role, you will design and implement a safety monitoring system for robotic platforms, utilizing advanced algorithmic tools to validate and enhance the performance
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Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods of research, testing and data
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, or the design of efficient, explainable, and scalable query engines. The successful applicant will help design and build novel systems and algorithms that challenge traditional assumptions in databases, guided by
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research team working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI
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seeking to recruit one post-doctoral researcher. Job Description The candidate will participate in the R&D activities of FORTH-IESL within the COLOURS project, working on the development of algorithms and
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Responsibilities: Conduct research on differential privacy algorithms with applications in computational processes. Develop innovative ideas to enhance existing algorithms and frameworks. Collaborate on applying
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dynamical systems. Designing learning-based event-triggered optimal control algorithms to achieve prescribed-time optimal output regulation for uncertain multi-agent systems. Investigating learning-based
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Bioinformatics and Computational Biology headed by Ivo Hofacker. Our team works on the development of algorithms and methods for problems in Computational Chemistry, Systems Chemistry, and Computational Biology
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Is the Job related to staff position within a Research Infrastructure? No Offer Description The researcher will use and further develop ptychography algorithms for transmission electron microscopy data