144 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" positions in Portugal
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/10.54499/2023.11234.PEX , funded by national funds through FCT/MECI, under the following conditions: Scientific Area: Machine Learning applied in Applied to Fluid Dynamics Simulation Admission requirements
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programme Reference Number AE2025-0509 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2025-0509
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tools, with particular focus on multi-threaded and distributed scenarios. Experience with observability tools, particularly OpenTelemetry. Solid knowledge and experience in machine learning, deep learning
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benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: ● Research and develop novel reliable deep learning computer vision algorithms for the detection and quantification of GIM lesions
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the following profile: Enrolled in a master’s degree in computer engineering or related fields; Knowledge of Extended Reality application development and/or knowledge of machine learning, information
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Faculdade de Ciências Médicas|NOVA Medical School da Universidade NOVA de Lisboa. | Portugal | 20 days ago
) Knowledge of machine learning, with the ability to support the validation and optimization of predictive algorithms applied to cardiac electrophysiology (25%); d) Knowledge of cardiac electrophysiology
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MSc in Electronic Engineering, Applied Physics, or a related field with a focus on Electrochemical Sensing and Data Science; Knowledge of machine learning methods and programming tools; Experience with
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of the state of the art in machine learning for generation of artificial data; - identify and select the appropriate methods for the study in question; - develop the research capacity through the application
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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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, for the period from 2021 -2026. Reference: BI/UTAD/116/2025 Scientific area/research field: Engineering Researcher profile: First Stage Researcher (R1) Admission requirements: Degree in Computer Engineering