43 machine-learning-and-image-processing-"RMIT-University" Fellowship positions in Portugal
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Education Institutions. Preference factors: Experience in research activities Minimum requirements: Knowledge of Computer Vision and Machine Learning 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS
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-award courses of Higher Education Institutions. Preference factors: Machine Learning Knowledge. Knowledge of signal processing and machine learning libraries (e.g., PyCaret, scikit-learn). Minimum
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domain in the design of deep learning algorithms for cardiovascular disease detection. 4. REQUIRED PROFILE: Admission requirements: Master's degree in Biomedical Engineering, Computer Engineering
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and/or attending a non-degree course taught by a higher education institution in collaboration with an R&D unit. Experience in image and audio capture (video and photography), video editing (Adobe
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(EBI), by the Norms for Grant Assignment and Management within R&D Projects, by the Code of Administrative Procedure (CPA), all in their current wording, and by other applicable national and community
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26 Aug 2025 Job Information Organisation/Company INESC TEC Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Country Portugal Application Deadline 8
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wastewater treatment plants, with the following main objectives: 1 - Model calibration through Machine Learning methodologies using process data. 2 - Development of a multimodal online sustainability
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2025-10-01. .WORK PLAN: a) Suitability of Additive Manufacturing equipment for processing conditions and parameter study; b) Functional testing and execution of test specimens and prototypes; c
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 10 days ago
, with application to biomedical flows (e.g. microcirculation) it is now intended to develop an improved algorithm for advanced particle tracking using machine learning. The algorithm should be tested in
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sequencing, proteomics, and metabolomics; interpretation of datasets and clinical data using advanced statistical methods and machine learning algorithms to identify correlations between molecular alterations