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flow behavior. The project also involves applying machine learning and computer vision techniques to enhance data analysis, pattern recognition, modeling, and prediction. The role requires a solid
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on the monitoring and response parts, building on many earlier projects revolving around the use of UAV/drones, computer vision and machine learning, change and damage detection, and multi-data integration, such as
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to carry out research using advanced theoretical and computational methods within quantum mechanics and statistical physics with the aim to study novel materials synthesized at extreme conditions. You will
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the Future 5G/6G Deployments with Millimeter Wave Integrated Circuit Interfaces Generated by Deep Computer Vision. This project is funded by FCT/MECI through national funds and when applicable co-funded EU
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learning, knowledge exchange, and joint knowledge creation. During these stays, 100% of the time of the PhD is dedicated to the research program. As part of NGinfra, the PhD candidate will also have ample
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Research Framework Programme? Horizon 2020 Reference Number BAP-2025-589 Is the Job related to staff position within a Research Infrastructure? No Offer Description The PhD researcher will: Carry out
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collection activities. Supervision will be provided by academics from various disciplines specializing in biomechanics, image processing, and computer vision, alongside orthopaedic surgeons and academics.
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requirements for doctoral studies, you must: hold a Master’s (second-cycle) degree in engineering physics, electrical engineering, machine learning, data science, computer vision, computer science, applied
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Application Deadline 20 Oct 2025 - 23:59 (Europe/Rome) Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Dec 2025 Is the job funded through the EU Research Framework Programme? Not funded by
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity