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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 2 months ago
will consider: Experience in applying advanced Machine Learning, Deep Learning, reduced-order models, or physics-informed methodologies to complex engineering systems (0–4 points); Experience in
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practices, and technology-enhanced learning. It is an advantage if you have one or more of the below A solid foundation in understanding learning processes from a cognitive, embodied, and/or epistemic
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to conduct research activities in the field of Brain-Computer Interfaces, in the scope of the project “BCI4ALL”, co-funded by national funds through the Portuguese Foundation for Science and Technology, I.P
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nutrition, such as: analysis of time series data and dynamic processes, where signals and responses evolve over time. statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and
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About the FSTM The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The Faculty of Science, Technology and Medicine
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, Microelectronics, Computer Engineering, or a closely related field, completed by the start of the position Have a solid background in digital hardware design: Verilog/SystemVerilog RTL, logic synthesis, and place
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16 Jul 2026 Job Information Organisation/Company UNIVERSITAT POMPEU FABRA Department Department of Economics and Business Research Field Engineering » Computer engineering Researcher Profile First
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to work collaboratively with others and contribute to a team environment. Technical Proficiency: Skilled in using office software, technology, and relevant computer applications. Communication: Strong and
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animal movement analyses. Geospatial analysis, GIS, and remote sensing. Statistical, ecological, and machine-learning modeling using modern analytical software (e.g., R or Python). Google Earth Engine and
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processes. • Apply machine learning techniques and advanced statistical analysis to extract knowledge from complex datasets. • Participate in the evaluation and optimisation of high-performance scientific