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biomedical image processing. Within the scope of machine learning and computer vision, there will be freedom to suggest your own research directions, and to become acquainted with new techniques and approaches
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Post-doctoral Researcher in Multimodal Foundation Models for Brain Cancer & Neuro-degenerative Disea
Qualifications PhD in machine learning, computer vision or a related field. Established expertise in deep learning methods applied to images analysis. Experiences with generative models, volumetric image
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-ranked university in Latin America, boasts a Computer Vision Group at IME-USP with over 20 years of experience in machine learning research and strong international collaborations. ** Application Process
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lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning techniques—including vision-language architectures (e.g., CLIP
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their work. Qualifications: PhD in Electrical and Computer Engineering, Biomedical Engineering, Mechanical Engineering or a related field. A combination of education and relevant experience from which
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techniques (FTIR, NIR, Raman, …) with machine learning/ chemometrics. Responsibilities of the Position The Postdoctoral researcher is intended to support the soil spectroscopy research activities and digital
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Requirements Applicants must hold a PhD degree in Machine Learning, Artificial Intelligence, Computer Science, Statistics, or a closely related field. A strong research background and programming experience
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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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FieldComputer science » OtherEducation LevelPhD or equivalent Skills/Qualifications CANDIDATE ’S PROFILE The candidate should possess a PhD in machine learning or computer vision and have a strong publication
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profile PhD in Computer Science, Data Science, Machine Learning, or a related discipline. Proven experience in computer vision (e.g. image processing, deep learning, object detection, segmentation) and