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23 Dec 2025 Job Information Organisation/Company INESC ID Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Master Positions Country Portugal
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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools
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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
(as machine learning techniques, etc.). Personal characteristics In the evaluation of which candidate is best qualified for the PhD position, emphasis will be placed on education, experience and
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-Development-Innovation is looking for a PhD-student with a doctoral grant. We invite applicants for a fully funded PhD position in machine learning, AI, and data science for public health within the Electronics
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, international group of 13 researchers from 8 countries, with expertise across energy systems and markets, optimization, control, game theory, and machine learning. Interdisciplinary by design: Work at the
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) and Machine Learning/NLP (Natural Language Processing) to capture both the network embeddedness and the qualitative B2B relationship features of supply chains. The project identifies key bottlenecks
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or machine learning. Activate map To activate the map, click on the "Show map" button. We would like to point out that data will be transmitted to OpenStreetMap after activation. You can find out more in our
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, electrical engineering or simila,r with an affection for machine learning; You are an independent and original thinker with a creative mindset; You are a fast thinker with excellent analytical and
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an allied field. An MSc degree in a relevant area is desirable though not necessary. Experience in coding (e.g., Python/R/Matlab) and experience in behavioural experimentation, statistics, or machine learning
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degree classification (20%); Proven experience in programming for Machine Learning in Python (20%); Proven experience in training and testing large language models in high-performance computing