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fields of study: Computer Science, Information and Computer Engineering, Software Engineering and Management, Mathematics, or equivalent disciplines Excellent programming skills Extensive knowledge of IT
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Admission Requirements Completed Master's degree or equivalent Diploma degree Preferred fields of study: Computer Science, Information and Computer Engineering, Software Engineering and Management
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/doctoral/renew-mat https://www.famnit.upr.si/en/education/enrolment . Specific Requirements Required additional functional competencies: computer literacy, organisational skills, ability to work effectively
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integrating AI with vision at the edge. Despite recent advancements, the synergy between AI and computer vision remains constrained by fundamental imaging bottlenecks. Conventional HDR techniques frequently
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system integrates robotics, automated sample handling, sensor networks, imaging systems, cloud computing, and machine-learning-based analytics. The research work at NTNU will focus particularly
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reduction, system identification, power electronics, model predictive control, multi-objective optimization, machine learning, renewable-energy integration, experimental testing, or hardware-in-the-loop
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doctoral degree Collect, structure and assess relevant sensor, operational, maintenance, incident and cost data Develop and validate statistical, causal and/or machine-learning methods and turn the results
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contribute to the development and validation of computationally efficient motor-twin models for permanent magnet synchronous machines. The work will focus on machine modelling, parameter and state estimation
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 3 months ago
- SIPG LAB/ISR and 1801P.01460.1.04 LARSYS/ISR BASE 2025-2029 – SIPG LAB/ISR, financed by national funds through FCT/MCTES Workplan: Develop new representations for computer vision and machine learning
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. In addition, the following are requirements for the role: Strong programming and quantitative skills, particularly in Python and/or R. Experience in deep learning, machine learning, or large-scale