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and sustainability; Investigate and apply artificial intelligence and machine learning techniques, including large language models (LLMs), across CENSE’s scientific body in its five thematic areas
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the European Union through the COMPETE 2030 Programme, of Portugal 2030, under the following conditions: Scientific Area: Machine Learning Admission requirements: Candidates who cumulatively meet the following two
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learning and deep learning applied to electroencephalography in the context of brain-computer interfaces, including experience with MATLAB and Python and in the design and conduct of experimental studies
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 2 months ago
engineering Engineering » Other Researcher Profile Recognised Researcher (R2) Positions PhD Positions Application Deadline 30 Jul 2026 - 11:59 (Europe/Lisbon) Country Portugal Type of Contract To be defined Job
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application d) Previous experience with computational modelling and machine learning. Additional optional skills and qualifications: Experience with Soft and Living Matter. Contracting requirements
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 3 months ago
additional days per year, including your birthday Minimum Requirements: PhD in Engineering Experience in Machine Learning, Deep Learning, reduced-order modeling, or physics-informed models Knowledge
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supporting documentation, proven experience in all of the following areas: Computer vision and video processing (ingestion, ROI, 2D/3D keypoints, heatmaps); Deep learning and temporal modelling (CNNs
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, machine learning, programming, software engineering, instrumentation, benchmarking, reproducibility and technological prototyping. The mandatory requirement for technical-scientific proficiency in English
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supporting documentation, proven experience in all of the following areas: natural language processing and machine translation (sequence-to-sequence modelling, NMT, glosses); deep learning, Transformers, and
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the numerical modelling of thermal phenomena, with particular emphasis on solidification processes. d) Knowledge on machine learning methods or data-driven modelling approaches applied to materials science or