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3GPP compliant 5G/6G NR NTN OFDM waveforms Develop and analyse signal processing and/or machine learning algorithms for joint channel, delay, Doppler and carrier phase estimation, remote object ranging
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in wireless communications and networking Background in AI and machine learning is an advantage. Experience and skills Knowledge of random-access protocols (e.g. IEEE 802.11 family). Understanding
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environment The Brain Imaging & Neuro Epidemiology group (BraINE) develops advanced methods for medical images analysis, combining computer vision, radiomics and deep learning approaches. The team has a double
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data-driven methods (optimisation, generative AI, agent-based modelling, machine learning). Our work provides decision support for policy makers, industry stakeholders, and researchers by delivering
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machine learning, deep learning, or computer vision Experience with Python and common AI frameworks (PyTorch, TensorFlow) Interest in hallucination detection, robustness, trustworthiness, and (optionally
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years of post-PhD research and engineering experience in AI for mobile security Solid knowledge in adversarial machine learning or trustworthy AI, including experience with robustness assessment and
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and optimization, we use tools such as artificial intelligence/machine learning, quantum conputing, graph theory, graph-signal processing, and convex/non-convex optimization. Furthermore, our
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-FNR PEARL Research Grant in the area of Information Systems Engineering, and, depending on interest, in fields, such as Generative AI & Machine Learning, Data Privacy, Cyber Security, Digital Identities
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, MONAI) Strong interest in image analysis / computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not
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, Python)—knowledge of machine learning/data science is a plus; Excellent communication and collaboration skills in an interdisciplinary and international environment; Fluency in English (oral and written