280 machine-learning "https:" "https:" "https:" "https:" "https:" "U.S" Fellowship positions in Portugal
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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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; - 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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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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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 6 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
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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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relevant areas (e.g., software engineering, cybersecurity, program analysis, machine learning), as evidenced by transcripts. Relevant professional or research experience in software security, static analysis
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(e.g., software engineering, cybersecurity, program analysis, machine learning). Relevant professional experience in software security, program analysis, or AI-driven code analysis. Scientific track
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submitted on the admissions platform of the Instituto Superior Técnico at https://fenix.tecnico.ulisboa.pt/fenixedu-admissions and requires registration and validation of the candidate's identity
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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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Engineering. INESC-ID’s research impact is focused on four Thematic Lines Energy transition Life and health technology Security and privacy. Societal digital transformation. INESC-ID promotes cooperation