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- UNIVERSIDAD POLITECNICA DE MADRID
- Computer Vision Center
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- Universidad de Alicante
- ARQUIMEA RESEARCH CENTER
- Autonomous University of Madrid (Universidad Autónoma de Madrid)
- Centre for Genomic Regulation
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- Fundació per a la Universitat Oberta de Catalunya
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- Institut Català de Nanociència i Nanotecnologia
- Institute for Bioengineering of Catalonia (IBEC)
- Institute for bioengineering of Catalonia, IBEC
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- UNIVERSIDAD DE LAS PALMAS DE GRAN CANARIA
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- Universitat Politècnica de Catalunya (UPC)- BarcelonaTECH
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flexible fitting of mechanistic and statistical models to the data. Our current focus is on generating datasets of sufficient size and diversity to train machine learning models to accurately predict how
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follow specialized training programs (e.g. ARENA). Specific Requirements Knowledge: Linear algebra, probability and statistics. Graph theory and algorithms on graphs. Machine learning and deep learning
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LevelMaster Degree or equivalent Skills/Qualifications Solid background in Machine Learning and Deep Learning. Experience or interest in agentic AI frameworks (e.g., LangChain, LangGraph, AutoGen, or similar
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intelligence, data science, medical physics, neuroimaging, bioengineering, or related disciplines, accompanied by accredited training in machine learning, deep learning, or medical image analysis. Experience: A
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y/o TFM en Visión por Computador, Procesamiento de imágenes, Machine Learning (incluyendo Deep Learning), Experiencia demostrable como programador en alguna de las áreas anteriores Artículos de
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for Languages (CEFR). Knowledge of Python programming. Knowledge of JavaScript. Practical experience in artificial intelligence, machine learning or deep learning, in academic or business environments. Training
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máquina/profundo aplicado al procesamiento de imágenes y vídeo / - Application of machine learning and deep learning techniques to image and video processing. - Aplicación de lo anterior al caso de uso
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aplicadas al procesamiento de imágenes y vídeo / - Application of machine learning/deep learning and image segmentation techniques to image and video processing. - Aplicación de lo anterior a casos de uso en
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for tactile perception and robotic grasping of objects. 3.3. Robot learning and deep learning techniques applied to robotic perception. 3.4. Techniques for reinforcement learning and learning by demonstration
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/Qualifications Skills in acoustics and audio. Prefereably skills in machine learning and deep learning. Specific Requirements Education in acoustics. Knowledge of acoustical measurement techniques, as