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Field
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Machine Learning. Work plan: Review of the state of the art on Evolutionary Algorithms and image tampering detection; Implementation of an evolutionary algorithm for image tampering detection
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spanning design, modelling and simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication
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6. of this Notice. Preferred factors: Knowledge in machine learning and programming (Python), deep learning (e.g., tensorflow, pytorch) and time-series modeling in marine ecology applications
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. Project Title: Machine learning techniques for crosstalk mitigation and new passive optical network architecturesHost institution: Iscte-IUL, PortugalThe LUMIRing project aims to deploy a MCF test bed
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large sample size; d. Experience in integration of multi-omics using machine learning approaches; e. Experience in participation of research teams or projects. Candidates must be enrolled in a doctorate
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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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. This scholarship aims to support PhD-level studies on FAE by exploiting a new methodology that combines deep learning technology and knowledge graphs. The goal is to research and develop a new Decision Support
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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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Institute of Systems and Robotics-Faculty of Sciences and Technology of the University of Coimbra | Portugal | 3 months ago
: Electrotechnical and Computer Engineering. Admission requirements: Students enrolled in a PhD. program in Electrotechnical and Computer Engineering, or in related areas, or alternatively, an MSc degree in
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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