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FOR DRAWING UP OF PREDOCTORAL CONTRACTS FOR THE TRAINING OF DOCTORAL STUDENTS FUNDED BY THE UPV'S RESEARCH STRUCTURES – SUBPROGRAMME 2 (PAID-01-22) 119865 Development of machine-learning and graph-based models
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networks and in mobile & edge computing platforms. Where to apply Website https://networks.imdea.org/job/phd-position-on-safe-machine-learning-msca-final… Requirements Research FieldEngineering » Electrical
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STUDENTS FUNDED BY THE UPV'S RESEARCH STRUCTURES – SUBPROGRAMME 2 (PAID-01-22) 119977 Development of mathematical and machine-learning algorithms to support an intelligent, integrated system for biosafety
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. Preliminary exposure to machine/deep learning, statistical modelling or generative AI. Application process: Interested candidates are invited to apply via the PHYNEST online platform by submitting a full CV, a
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participative digital platform that encourages users to contribute through crowdsourcing, oral interviews, or sharing privately held materials. Implementation of AI and machine learning techniques, including
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theory and experiment. By integrating quantum optics, quantum information theory, and machine learning, the project seeks to establish scalable hybrid spin–orbit quantum links. Free-space connections
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scalable and efficient software and firmware analysis methodologies using tools like Frida or IDA Pro, enhanced with traffic monitoring techniques and Machine Learning to detect and analyze vulnerabilities
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to apply Website https://www.cttc.cat/job/call-24-2025-1-senior-researcher-position/ Requirements Research FieldTechnology » Telecommunications technologyEducation LevelPhD or equivalent Skills
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classification of EEG and auditory signals. The group of the project is multidisciplinary, with experts in signal processing, machine learning, acoustics and language. The successful applicant will perform
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remuneration, or a proportional part for periods of less than one year. 2.5. Tasks to be carried out: · Developing advanced tools for the spectral analysis of the X-IFU instrument. · Developing machine learning