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Field
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, and inline interpretation. The work includes validating algorithms on a lab-scale sorting setup and linking spectroscopic outputs to recyclability scoring and Digital Product Passport data flows. Your
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algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we are looking for The following requirements are mandatory: A doctoral
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networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis
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development, networking, administrative and technical support functions, along with good employment conditions. More information about the department is available at: https://www.umu.se/en/department
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
to develop/translate model algorithms and develop new model code in Fortran, d) software skills needed to work with multiple observed and model datasets, e) a strong desire/motivation to develop scientific
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imaging, computer vision, and predictive modelling. The postdoc will further develop an existing rumen‑fill scoring algorithm into a functional prototype and pilot the technology for longitudinal monitoring
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algorithms, architectures, and learning strategies that fundamentally challenge existing resource constraints in large-scale AI systems. Prototype, implement, and rigorously evaluate complex machine learning
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University Abu Dhabi, seeks to recruit a motivated research or postdoctoral associate to lead the design, development, and validation of material, control systems, and algorithms for next-generation soft
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and paleosols 3) train and test deep learning algorithms. You will be required to take responsibility for all the steps involved in the “Phytolith analysis” work package of DEMODRIVERS. This will
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these autonomy and self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed