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with e-CALLISTO instruments or Software-Defined Radios (SDRs). · Familiarity with machine learning for astrophysical data analysis. · Knowledge of solar radio data pipelines and event classification
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possible renewals, an accumulated period of 3 (three) years in this type of scholarship, consecutive or interpolated. Proven knowledge in: Data Science (Python) Machine Learning (Python) Remote Sensing
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from academic degree recognition processes. Preferential factors: a. Knowledge of developing artificial intelligence/machine learning (AI/ML) models and classifiers suited for embedded systems
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Area: Computer Science 2. Admission Requirements: Graduates (Licenciatura) in computer engineering or related area, with experience in Machine Learning/Deep Learning methods/techniques. 3. Project
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experience in Data Science/Machine Learning projects or initiatives (professional projects, coursework, internships, personal projects or hackathons, etc.) Knowledge and experience with the use of tools
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: Research Field: Machine Learning/Pattern Recognition Objectives: AI-based module for energy consumption and quality control optimization. Work plan: The planned work involves collaboration in the process of
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: Research Field: Machine Learning/Pattern Recognition Objectives: AI-based module for energy consumption and quality control optimization. Work plan: The planned work involves collaboration in the process of
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engineering Engineering » Computer engineering Engineering » Knowledge engineering Engineering » Simulation engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Portugal
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Engineering, Biomedical Engineering (Medical Informatics), or related areas. Recipient category: Masters, enrolled in the course: Degree courses: enrolled in doctorate. Non-conferring degrees courses: enrolled
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(spoken and written), academic excellence, autonomy, curiosity, and attention to detail. Resumes demonstrating knowledge of programming in Python and/or Matlab; computer vision, image processing, learning