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Field
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incorporates probabilistic prediction models and hybrid optimization and machine learning techniques. This approach will enable the efficient assessment, planning, and offering of flexibility in scenarios
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apply statistical and machine learning models to identify predictive markers of depression. Design and execute experimental protocols related to self- representation and chronic pain. Prepare manuscripts
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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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research 0 A5 Experience and training in managing techniques and methods necessary for the execution of the project: - Computational neuroscience and machine learning 20 A6 Stays at universities and/or other
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implementation of artificial intelligence models (machine learning, deep learning, and adaptive learning) applied to the sensorimotor control of a bionic arm prosthesis. • Advanced processing of neural (EEG) and
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fields. Relevant Training or Courses (optional): Experience with R or similar programming tools, or machine learning algorithms. Geographic Information Systems (GIS). Basic and advanced statistics, time
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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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of machine learning tools in the process industry - General research tasks (scientific article writing, oral presentation of results, document management, etc.) - Technoeconomic analysis and life cycle
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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