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data and archaeological signatures (e.g., circular mounds, linear ditches, rectangular foundations, etc.) tailored for AI applications – Feature engineering and representation learning to enhance
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edge devices in distributed and collaborative IoT scenarios; ii) strategies for efficient and adaptive learning on-device or across a network of heterogeneous nodes while minimizing energy consumption
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to learn Spirit of innovation and creativity Good in time and priority management Ability to work in a challenging and international environment Capacity to work autonomously and collaboratively in a highly
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. The application scenarios range from quality-control, video-surveillance to automotive. The company collaborates with Fondazione Bruno Kessler in the study and development of advanced low-power
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look for correlations between transient phenomena in the ionosphere and seismic events. The successful candidate will develop and apply state-of-the-art machine learning techniques to enhance
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environment and in a multidisciplinary and multicultural team. The JRC encourages a collaborative workplace culture and interactions with policy makers, academics and other leading stakeholders active in
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, Python or similar) Specific Requirements Interest in optical imaging and interferometry. Prior knowledge will be a plus Interest in computational methods, image processing and machine/deep learning. Prior
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analog, requiring the model to integrate multimodal inputs to anticipate the onset and spatial evolution of ionospheric storms. The successful candidate will work at the intersection of deep learning and
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the structural and electronic properties of complex materials. Using first-principles simulations, machine learning techniques, and advanced Monte Carlo methods, the student will develop predictive
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universities, research institutes, and industrial partners. The programme provides an interdisciplinary research environment combining advanced experimental research, theoretical collaboration, international