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with European industry. The EISLAB division at Luleå University of Technology conducts research in electronic systems design, sensor systems, cyber-physical systems, the Internet of Things and machine learning
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research with skilled colleagues who share your interests? Maybe you can become our new colleague at the Department of Electrical Engineering, IT and Cybernetics at the Faculty of Technology, Natural
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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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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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. The EISLAB division at Luleå University of Technology conducts research in electronic systems design, sensor systems, cyber-physical systems, the Internet of Things and machine learning, and works on
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 3 months ago
applications will be tackled. The first concerns electrical machines like power plant alternators for which EDF has developped numerical simulators and has large sets of sensor data (measuring leaking flux
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to apply Website https://www.iesl.forth.gr/en/about/job-positions/2026226723 Requirements Research FieldEngineering » Electrical engineeringEducation LevelPhD or equivalent Skills/Qualifications Good
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observations and the current climate, so that we can describe the effects of climate change and the impact to the financial sector? Are you our new colleague? We are looking for a PhD candidate to develop AI
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Current University of Arkansas System employees, including student employees and graduate assistants, need to log in to Workday via MyApps.Microsoft.com , then access Find Jobs from the Workday
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | about 1 month ago
worldwide is projected to approach 180 zettabytes (ZB) per year by 2025 [1]. However, current storage technologies face significant limitations in scaling sustainably to such volumes. One promising solution