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. This involves the development of mathematical models for signal transmission/reception, derivation of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or
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modeling techniques and artificial intelligence methodologies in brain diseases. The candidate will work on developing advanced new algorithms, testing and validation, and applications in these data
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pathways, including deactivation processes. Screening and fine-tuning catalysts to enhance performance. Developing workflows and machine learning algorithms to accelerate catalyst design (optional). Group
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work hands-on with clinical data and build robust deep learning algorithms. We welcome applications from individuals with experience in: Experience developing deep learning models for real-time image
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into FE codes for application in process optimization studies Utilize ML algorithms and optimization frameworks in conjunction with FE simulations to develop process optimization framework for metal/ceramic
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across diverse scenarios, we aim to develop innovative algorithms that dynamically balance the improvement of speech intelligibility, sound localization, and environmental awareness, dependent
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teams to contribute to the development of fundamental aspects of computer science (models, languages, methods, algorithms) and to develop synergy between the conceptual, technological and societal
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In this role, you will help develop and implement cutting-edge AI solutions for real-time, image-guided medical applications, with a focus on advanced robotics. You will work directly with clinical
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on two types of cultural heritage objects: medieval parchments and antique coins. PHOENIX aims to develop a methodology based on physicochemical characterizations capable of addressing common objectives
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for three consecutive periods (2014-2018 and 2018-2022 and 2023-2026). ICN2 comprises 19 Research Groups, 7 Technical Development and Support Units and Facilities, and 2 Research Platforms, covering different