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interfaces under reaction conditions, using machine-learned interatomic potentials (MLIPs) for automatic reaction network exploration for catalyst dynamics, and developing the next generation of kinetic Monte
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journals and conferences such as Journal of Machine Learning Research (JMLR), Transactions on Machine Learning Research (TMLR), NeurIPS, ICML, RLDM, ICLR, IROS, or other top IEEE venues. Experience with
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A confluence of technological milestones in detector, computation and machine learning development, both globally and within the NRC, makes high-speed non-linear wavefront sensing for Astronomy
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approaches for binarized network models, identifying their strengths, limitations, and applicability within privacy-focused machine learning frameworks. Special attention will be given to evaluating
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of cellular behavior to advance gene and cell therapies — including CAR-T cell therapy and muscle stem cell regeneration. The successful candidate will apply machine learning, statistical modeling, and single
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, green finance, ethical supply chains, and behavioural change. Digital and Technologies: AI and machine learning, cybersecurity, spatial intelligence, robotics, human-computer interaction, and digital
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Requirements: Applicants must be enrolled in a Master’s Degree in Computer Engineering or related fields. Proof of enrolment must be provided by the time of contracting. However, candidates may initially submit
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6 Nov 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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Institute of Systems and Robotics-Faculty of Sciences and Technology of the University of Coimbra | Portugal | about 2 months ago
. Candidates should possess a strong background in power systems, multi-objective optimization and control (particularly MPC), and machine-learning–based time-series forecasting, along with proficiency in MATLAB
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.; - Develop skills in artificial intelligence and machine learning techniques for analyzing operational data and detecting anomalies, using foundational model approaches (e.g., GridFM project, LF Energy