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architectures for foundation models The work will combine methodological development with large-scale experiments, aiming for contributions at leading machine learning and computer vision venues such as NeurIPS
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and hardware security assurance for embedded systems by combining advanced side-channel analysis, fault-injection techniques, AI- and machine-learning-assisted analysis, robustness evaluation, and
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 24 days ago
: Good programming skills, preferably in Python, and strong analytical abilities. A solid background in machine learning, computer security, privacy, distributed systems, or a closely related area
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Machine Learning within the School of Medicine at the University of Limerick. This is a methodologically focused PhD for candidates with strong quantitative backgrounds who wish to develop novel statistical
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willingness to acquire advanced longitudinal and machine-learning methods. Programming skills in R (or a demonstrated ability to acquire them quickly). An interest in working with large, real-world data from
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memory safety, supply chain security and machine learning Independent teaching Student supervision and academic publications Training and professional development Organisational and administrative duties
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, Econometrics, and Equilibrium, Expectations & Dynamics (CeNDEF). Find more information … Where to apply Website https://www.academictransfer.com/en/jobs/363541/phd-candidate-in-aidriven-flood… Requirements
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students to analysing multimodal learning data (e.g., individual as well as collaborative verbal interactions, student gestures, task and activity sequences) and evaluating long-term learning outcomes. As
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statistical model calibration, machine learning and data analysis. The research environment is international and interdisciplinary, with close links between fundamental method development and technically
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partners in the Netherlands and abroad. You will work with large, multi-center clinical datasets and contribute to translating advanced computational methods into clinically relevant applications. This PhD