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learning pipelines for multilayer segmentation and nanoscale transistor classification from microscopy images; (b) Designing graph-based inference models capable of reconstructing higher-level logical blocks
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unit: Institute of Computational Perception Entry date: 10/01/2026 Job Reference Number: 34400-2026-002088 Job Duties: Conduct independent research in the fields of recommender systems, user modeling
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
-tier machine learning and computer vision venues, actively participating in departmental seminars, and contributing to collaborative projects. Where to apply Website https://jobs.inria.fr/public/classic
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, machine learning, data-driven dynamics, stability/perturbation theory/rigorous numerics. Possible applications of the new theory include the analysis of spatiotemporal data arising from weather and climate
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Scholarship of Teaching and Learning (SoTL) Laboratory to design, build, and maintain a next-generation learning platform that leverages large language models, AI agents, and cloud technologies to support
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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that is streamlined, thoughtful, and trusted. We envision a model where individuals think beyond their roles, teams are fully engaged, and the organization takes shared ownership of outcomes, challenging
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Dipartimento di Ingegneria dell'Informazione - Università degli studi di Padova | Italy | about 2 months ago
datasets, the development of machine learning models, and performance evaluation in industrial application scenarios. Where to apply Website https://pica.cineca.it/unipd/ Requirements Additional Information
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statistics, AI and machine learning methods, including demonstrated experience in analysing multiple global change drivers, e.g. land use intensity, climate change, nitrogen deposition. Proven capability
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energy systems and demand response; process and energy system modeling; optimization and model predictive control; and applications of artificial intelligence and machine learning to energy systems