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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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techniques and machine learning algorithms. The goal is to decipher patterns in the bioelectrical signals. Project UID-50008-2025 funded by FCT-Fundação para a Ciência e a Tecnologia. Scientific Area: Basic
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. Preferential Factors Knowledge of machine learning, information retrieval, and natural language processing. Experience with conversational models and systems. Knowledge of Large Language Models (LLMs) and Large
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. Preference factors: - Outstanding academic performance in subjects such as Machine Learning and Artificial Intelligence; - Demonstrated knowledge and skills in Data Science or HCI (Human-Computer
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reproduction; 5. Experience in light microscopy and image analysis techniques (machine learning – FIJI, deep learning). Non-compliance with these requirements invalidates the application. Provision of false
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learning methods; Experience with multivariate time series. Minimum requirements: Enrollment in a master’s program in Computer Science or a doctoral program in computer engineering or a related field, or in
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 5 days ago
in environmental or territorial studies. Preference will be given to candidates with aptitude for working in disaster risk management environments supported with GIS and Machine Learning, basic
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hemocompatible coating strategies to improve membrane–blood interactions. - Model and optimize membrane performance using computational tools, machine learning, and artificial intelligence Work Plan - Synthesis
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: Alexandre José Malheiro Bernardino (ist13761) Organic Unit: Scientific Area of Systems, Decision and Control Scholarship Theme: Computational Auditory System Simulators and Machine Learning-based Optimisation