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within the framework of project 2023.14874.PEX - “anomaly - Machine Learning-based Models for Advanced Anomaly Detection in Dam Structural Health Monitoring”, with DOI https://doi.org/10.54499/2023.14874
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: Department of Civil Engineering, University of Coimbra III- Scientific supervision/coordination of the grant: Luís Alberto Proença Simões da Silva IV - Work Plan / Goals to be achieved: Based on the project
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buildings, based on the design of a provided plan. The type of building may also be given as input, as well as various rules such as density of interior elements, number of windows, number of doors and
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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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, using experimental in vitro (cell lines) and in vivo (animal models) models. Work plan: Identify novel blood-based biomarkers for Parkinson’s disease, focusing specifically on microRNAs and their carrier
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ASSOCIACAO ISCTE CONHECIMENTO E INOVACAO - CENTRO DE VALORIZACAO E TRANSFERENCIA DE TECNOLOGIAS | Portugal | 8 days ago
pedagogical models for higher education, digital health solutions, and capacity building in the digital health sector; Excellent command of the English language; Proactive attitude, critical thinking
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biosynthetic pathways for aniline production in microbial hosts. Responsibilities include generating and enumerating potential pathways, followed by their prioritization based on criteria such as pathway length
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position within a Research Infrastructure? No Offer Description A tender is hereby opened for the award of 1Research Grant(s) (RG) in the scope of R&D project CardioChip, “Advancing Disease Modeling and Drug
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, tissue and organ abnormalities, and disease progression. Creation of novel disease models. Objectives: To establish in vitro microphysiological disease models based on human cells and tissues derived from
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to accelerate climate action and resilience in agriculture by conducting a systematic assessment based on the downscaling of integrated assessment model (IAM) climate scenarios; 2. Characterise the techno