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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 2 months ago
. The successful candidate will be expected to carry out the following scientific activities: Application of advanced Machine Learning, Deep Learning, reduced-order modelling, and physics-informed/-guided modelling
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candidates using photometry, spectral energy distributions and, when available, spectroscopic data. Both fellows will produce diagnostic plots and summary tables. Simple statistical or machine-learning methods
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Center, Corporate Partnerships, Alumni, Intellectual Property, Entrepreneurship). This grant offers the fellows the opportunity to engage in scientific activity and learning within the various areas
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, decentralized authentication, trust management, secure communications, Artificial Intelligence-based intrusion detection, and adversarially robust machine learning techniques.; ; Furthermore, the fellowship seeks
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of ecosystem degradation and the aforementioned extreme climatic phenomena; (v) explore a “Machine Learning” analysis to explore the importance of other environmental and managerial factors in the spatial and
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INESC COIMBRA - INSTITUTO DE ENGENHARIA DE SISTEMAS E COMPUTADORES DE COIMBRA | Portugal | 3 months ago
algorithms, population-based methods, hybrid strategies, and decomposition approaches. The integration of machine learning and surrogate modeling techniques will also be explored to accelerate the optimization
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control, statistical analysis and critical interpretation of results.. Preferred Factors: Will be considered preferred factors: -• Knowledge of machine learning and advanced data analysis, including
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courses of Higher Education Institutions. Preference factors: • Fluency in English and Portuguese, spoken and written; • Experience in data analysis, Artificial Intelligence or machine learning
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1 (one) Research Fellowship(s) for R&D Initiation under Special Regime (BII-RE) for Bachelor Student
requirements: Knowledge in Computer Science Additional optional skills and qualifications: Knowledge of Python programming, machine learning, natural language processing, databases, life sciences, or biology
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. - Criterion 2: Knowledge in the scientific areas of the project: Academic or applied knowledge in Software Engineering, Intelligent Systems/Machine Learning, and Interactive Technologies. - Criterion 3