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- Fundació Hospital Universitari Vall d'Hebron- Institut de recerca
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- Computer Vision Center
- Fundació Privada Institut d'Investigació Oncològica de Vall d'Hebron (VHIO)
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to develop and implement machine learning/deep learning tools for personalized medicine in cancer by exploiting electronic medical records and medical images in relation to cancer diagnosis and the
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regulated training activities and contribute to continuous training activities. Conduct research that allows the development of new AI methodologies based on deep learning that allow for assisting musical
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. Familiarity with statistical modelling, machine learning and deep-learning Additional information: We offer: 🌐The opportunity to work with our state-of-the-art HPC infrastructure and to join a vibrant network
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the LAMP group at the Computer Vision Center (CVC), in Barcelona, Spain. The position is for 2-3 years and linked to the project “Foundations for Adaptive and Generalizable Deep Learning” (EXPLORA
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. SILEX 2025) to calculate the Fire Radiative Power (FRP) and compare with satellite observations (VIIRS, SLSTR, FCI). Develop a fire front segmentation algorithm using machine learning techniques (deep
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or equivalent Research FieldEngineering » OtherEducation LevelPhD or equivalent Skills/Qualifications Skills in acoustics (PhD in acoustics required) and acoustics software. Skills in machine learning and deep
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resonance imaging) Fluency in English Experience and knowledge: Required: Experience in computer programming Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow
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resonance imaging) Fluency in English Experience and knowledge: Required: Experience in computer programming Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow
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of inflation and phase transitions in the early universe. We are developing new data analysis methods like the use of deep learning and the use of robust statistics. This work is naturally extended to studying
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of inflation and phase transitions in the early universe. We are developing new data analysis methods like the use of deep learning and the use of robust statistics. This work is naturally extended to studying