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Engineering or related fields. Be enrolled in a doctoral program in the scientific area of Electrical Engineering. Willingness to work in the developing of signal processing and machine learning algorithms
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: Pattern and Image Analysis – Lx Work Objectives: - Evaluate and compare different causality discovery algorithms under controlled and realistic conditions. - Develop a robust and interpretable causal model
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Development and Operations Phase (IV023”, funded by EUMETSAT, a Agência Europeia para a Exploração de Satélites Meteorológicos, under the following conditions: Scientific Area: Geosciences Admission
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Master Degree or PhD holder in the scope of the R&D Project: FireSys, funded by the European Regional Development Fund (ERDF), through the Innovation and Digital Transition Thematic Programme (COMPETE 2030
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consumption and power distribution in multi-source shipboard energy systems.; ▪ Develop a Digital Twin framework for real-time simulation, monitoring, and performance validation of the proposed architectures
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Objectives - Develop hemocompatible hybrid and composite membranes for gas permeation. - Integrate Metal–Organic Frameworks (MOFs) to enhance oxygen and carbon dioxide permeability and selectivity. - Design
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algorithms for anomaly detection and predictive maintenance of subsea structures Duration: 6 months Maximum Duration Including Renewals: 12 months Objectives Development of AI-driven models to identify
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implementation of methods, algorithms, and applications for the Portuguese use case. - Specification and development of the Portuguese pilot implementation. - Active participation in meetings with
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for planners and public authorities. Accordingly, the work plan includes: 1. Analysis of image databases classified according to the main construction attributes; 2. Development and calibration of AI algorithms
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Advanced Remote Sensing Technologies, under the project reference no. 2023.16642.ICDT/COMPETE2030-FEDER-00886500. The main objective of this research work is to develop algorithms and methods combining multi