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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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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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artificial intelligence-based algorithms to optimise operation and predict anomalies in water distribution networks. The algorithms developed should identify patterns and anomalies that indicate the presence
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until the time of signing the contract. Activity Outline: Grant holders will work on the following topics TASK 3: Empirical Analysis of Deep Learning and Generative Modeling Algorithms TASK 4: Development
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Recovery and Resilience Plan (RRP), on the following conditions: Work Plan and Objectives to Reach: The plan to be developed will consist of: • Developing and maintaining prescriptive data analysis
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- QKD, under the scope of a term contract, with a termination date undetermined, overseen by Portuguese Labour Code. Job description: Development of scientific research activities within the research
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Solution to Improve Care and Optimize Resources for Frequent Emergency Users”, (ref.ª 2024.07543.IACDC, financed by "RE-C05-i08.m04 – "Support the launch of a program of R&D projects aimed at the development
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Institute of Systems and Robotics-Faculty of Sciences and Technology of the University of Coimbra | Portugal | 7 days ago
-FEDER-00818200 and the acronym flexREC, financed by FCT, the European Regional Development Fund and national funds through the program MPr-2023-12l, in the following conditions: Scientific area
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Artificial Intelligence, with an emphasis on developing methodologies and techniques for Evolutionary Computation and Machine Learning. Work Plan: State-of-the-art survey of Evolutionary Algorithms and Large
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clinical data and machine learning algorithms. The main activities include: Support for AI Model Development: • Collaborating on the training of predictive models under the supervision of the scientific team