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Fundación para la Investigación Biomédica del Hospital Gregorio Marañón (FIBHGM) | Spain | about 19 hours ago
, scikit-image, SimpleITK, etc.), will be given special consideration. Knowledge of machine learning and artificial intelligence techniques is a plus. Previous experience in code parallelization, the use
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. Fluency in English (C1 level or higher). Hands-on experience with Python and AI/ML frameworks (e.g. PyTorch, TensorFlow, HuggingFace). Solid foundation in machine learning, deep learning, and neural network
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LevelBachelor Degree or equivalent Skills/Qualifications Experiencia investigadora. Colaboración grupos 5%, Publicaciones 5%. Conocimientos en análisis de datos o Machine Learning. 10%. Experiencia C++/Python 10
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: Algorithm. Software architecture. Machine learning. Professional Experience: In programming and/or research, image processing, machine learning. In topics of machine learning applied to wireless
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of Barcelona; Particle Physics Phenomenology group. Main responsibilities / tasks: 1. Develop anomaly detection methods using Machine Learning and Simulation-Based Inference for high-dimensional parameter spaces
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specifically in Biotechnology or degrees directly related to biomedicine. (MECES Level 2). Specific Requirements Professional experience in Machine Learning, accredited through a certificate of services and
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and modelling of omics, clinical and imaging data, development of reproducible pipelines, application of machine learning techniques, integration of multi-modal data, scientific publication and
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engineering or similar. Knowledge and experience with deep learning models applied in computer vision. Remarkable academic trajectory, validated by a strong record of publications in relevant international
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Python. Other requirements to consider: Machine Learning knowledge. Specific Requirements Knowledge: Fluid Mechanics. Computational Fluid Mechanics (CFD). Aerodynamics. Wind turbines. It will be assessed
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selection process for an indefinite employment contract within the framework of a Line of Research: R&D line: Machine Learning Applications https://www.upv.es/entidades/SRH/conypi/A1271105.pdf Where to apply