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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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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 18 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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; Knowledge of Machine Learning; Knowledge of mobile application development; Ability to interpret results and analyse data; Experience or motivation to join working groups and research networks. Workplan and
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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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, with a view to developing and carrying out the above-mentioned project and related scientific activities, with a particular focus on the development of analytical models (data science – machine learning
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | about 1 month ago
conditions: Scientific area: Bachelor’s Degree in Systems Engineering. Work plan: Model-based digital twins supported by physics-informed machine learning for multiphysics modeling. 1. Grant duration and
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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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16 Feb 2026 Job Information Organisation/Company Instituto Pedro Nunes Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Bachelor Positions
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-of-the-art models for computer vision based on Machine Learning. Work plan: - Analysis and study of existing resources. - Analysis of the state of the art in universal adversarial attacks on computer vision