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and IT, to develop techniques for planning the work of Agri robots, scheduling their tasks, deploying IoT devices in fields as well as for collecting, integrating, storing, analysing, building
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sustainability, particularly in locations where environmental conditions and load patterns change significantly throughout the year. A key challenge is developing resilient PV and battery systems — solutions
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predict food-effector systems. Key Responsibilities • Develop graph-based (multi-)omics analysis algorithms • Benchmark graph-theoretic against graph-ML approaches • Analysis of food-related (multi-)omics
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maintaining human oversight for quality and accuracy. This real-world case provides a unique opportunity to study how trust in AI systems develops, whether specialists seek to preserve authority in decision
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UNIVERSIDAD CATÓLICA DE MURCIA - FUNDACIÓN UNIVERSITARIA SAN ANTONIO DE MURCIA | Spain | 3 months ago
, development, and training of machine learning and deep learning algorithms. Creation of accurate, robust, and energy-efficient models. Development of systems capable of predicting and making decisions in real
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to the sustained development of the Internet by providing cost-effective cybersecurity and privacy technologies. This will equip IoT infrastructures with the necessary protection levels for the sustainable
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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uncertainties, with a strong focus on safety-critical bilateral teleoperation (e.g., tele-surgery). The doctoral research will focus on the development of fault-tolerant and adaptive control architectures
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Institute of Low Temperature and Structure Research Polish Academy of Sciences | Poland | 21 days ago
. The resulting data will be used to train a machine learning algorithm to predict the pressure-induced variation in the luminescent properties of phosphors co-doped with Cr3+ and Mn4+ ions based on the structural
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algorithms and methods for calibrated Bayesian federated learning for trustworthy collaborative Bayesian learning on data from multiple participants. The project will develop new methods, theory, and