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work environment. We are seeking a part-time Research Assistant in computer vision and machine learning for human behavior analysis and modeling. The successful candidate will investigate new algorithms
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worthiness of autonomous vehicles cyber risk management, advanced threat intelligence secure-by-design for IoT and policy governance of cybersecurity For more details, please view https://www.ntu.edu.sg/cysren
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Grundstufe (praedoc) Limited until: 30.04.2029 Reference no.: 5311 Where to apply Website https://iventajobdata.eu/bestmedia/img/2284472/1912258/cl/5cf77b097cc38f6987463… Requirements Specific Requirements
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mathematical theory and algorithm development as well as engineering methods that enable robust and efficient practical solutions. As society and technology evolve toward increasingly large‑scale, data‑intensive
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-transparent materials, and the utilisation of deep learning algorithms to accelerate computational solutions. Scientific Objectives Develop a self-contained finite volume solver for solidification of multiphase
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scheduling algorithms to ensure operation continuity and reliable information exchange among batteryless sensors. Where to apply Website https://lavoraconnoi.unitn.it/research-assignments/dipartimento-disi
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region of Portugal, although other tasks may be considered according to the profile of the recruited researcher. The researcher will be responsible for implementing spatial analysis algorithms, developing
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platform integrating advanced robotics, materials characterization systems, digital twins and AI-based decision-making algorithms for accelerated research. The platform will incorporate explainable AI
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 11 days ago
. Main activities : – Read papers and state of the art - Benchmark existing algorithms – Write problem formulation, proofs of convergence. – Adapt the formulation to the target scenario. – Propose a new
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, to create a unified and reliable representation of structural integrity. The work expands on TU/e’s contributions by developing algorithmic components for detection and classification of defects and anomalies