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28 Aug 2025 Job Information Organisation/Company University of Aveiro Department DETI Research Field Other Researcher Profile First Stage Researcher (R1) Positions Master Positions Country Portugal Application Deadline 12 Sep 2025 - 23:59 (Europe/Lisbon) Type of Contract Not Applicable Job...
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Project CARMA – Refª 101168355 - BI|2025/749 Is the Job related to staff position within a Research Infrastructure? No Offer Description Public notice for research grant Refª BI|2025/749 Project CARMA
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months only to find they are worthless at harvest. This project aims to develop new capability to detect and measure volatile agents directly in smoke, allowing growers to make more cost-effective vineyard
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) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: Applying anomaly detection algorithms for streaming network data. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND
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) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: CAD systems for the automatic detection of pulmonary nodules on CT are one of the most studied applications
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holders" (https://www.inesctec.pt/pagamento-propinas-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: The main objectives of the Grant
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/pagamento-propinas-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: The overall vision of the ATE is to deploy and demonstrate a set of business models
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. Activity Outline: The objective, within WP8 - ‘Waste Smart Management’, is to explore the role of brands in the context of the circular economy, exploring their influence on the circulation, valorisation and
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. Activity Outline: The objective of this research, within the scope WP8 - “Waste Smart Management”, is to explore the behaviour of consumers in what regards circular economy practices. This includes
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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