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for TCGA breast cancer data. - Establish the groundwork of causal structures aiming to improve the generalization and interpretability of predictive models. Place of work: The work will be developed
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algorithms for anomaly detection and predictive maintenance of subsea structures Duration: 6 months Maximum Duration Including Renewals: 12 months Objectives Development of AI-driven models to identify
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programme Reference Number AE2026-0090 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2026-0090
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image data; Experience in data analysis programs, with special focus on the R programming language; Experience in the development and validation of predictive models applied to the health area
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the University of Porto (FEUP), under the scientific supervision of Professor Filipe Mergulhão. Grant duration: Duration of 8 months, with the predicted starting date in May 2026, on an exclusive basis eventually
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Miguel Magalhães Mendes and Doctor Dzmitry Ivanou. Grant duration: Initial duration of 6 months, with the predicted starting date in July 2026, on an exclusive basis eventually renewable but never
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(FEUP), under the scientific supervision of Dr. Vítor Jorge Pais Vilar and Dr. Maria Francisca da Costa Moreira. Grant duration: Initial duration of 08 months, with the predicted starting date in
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Rodrigues da Cruz. Grant duration: Initial duration of 12 months, with the predicted starting date in May 2026, on an exclusive basis, eventually renewable but never exceeding the project duration. If it is
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the predicted starting date in May 2026, on an exclusive basis eventually renewable but never exceeding the end of the project. If it is not possible to ensure the 6-month duration provided for in the previous
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a Research Infrastructure? No Offer Description Work Plan • Survey of the state of the art in predictive and analytical models applied to sports, ranging from statistical and linear approaches to deep