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of a call for awarding a research fellowship (RF) in the scope of the research project AQUALEARN – Machine learning-based digital twins for real time anomaly detection in water supply systems. 3
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fellowship: Novel microfluidics and machine learning tools for unravelling environmental microbiomes Within the remit of the ERA Chair project REACTORS 5.0 “Sustainable REACTOR and microreactor designS
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; - academic curriculum with background on the computer science and machine learning background; - previous research and professional experience on the scientific domains of the work. Additional Information
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 5 days ago
). Experience in machine learning methods, including data preprocessing, model selection, and performance evaluation. Familiarity with programming languages commonly used in these domains (e.g., Python, MATLAB
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experience – 40% Evaluation of academic performance and/or relevant professional experience in machine learning, AI, software engineering, or security-related domains. Research track record and scientific
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following use cases: • The construction of a machine learning pipeline that allows the conversion of Course Unit Sheets (CUS) into a data structure based on the European Learning Model (ELM). • Integration
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while protecting data privacy. Unlike traditional centralized machine learning, where data must be collected and stored in a central server, FL allows multiple parties to collaboratively build a global
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: MSc degree completed. Additional optional skills and qualifications: Previous research experience, particularly in the fields of Internet of Things security and machine learning models applied
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' performance will be assessed according to the following weights and criteria: - Criterion 1 - Knowledge in the areas of Bioinformatics, Artificial Intelligence and Machine Learning - Criterion 2 – Motivation
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 19 days ago
, financed by EU and national funds through FCT/MCTES (PIDDAC Workplan: Machine learning algorithms, in particular deep learning ones, suffer from the phenomena of catastrophic forgetting, which hampers