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, controllers, and Internet of Things (IoT) network equipment. - Working knowledge of Simple Management Network Protocol (SNMP) management tools, performance measurement tools, and planning tools and techniques
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the exploration of techniques based on physics-informed neural networks and transfer learning. The PhD candidates will have the opportunity to be affiliated with the program BRU21 , with the IoT@NTNU, and with
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programme Reference Number AE2026-0210 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2026-0210
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: https://www.list.lu/ How will you contribute? You will be mainly in charge of: Working with real-time simulators (OPAL-RT) with hardware in the loop functionalities to test and validate AI-based solutions
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/ Strong background in Infocomm Technology for IoT implementation and integration Successful candidates will join the Engineering (ENG) Cluster (one of five academic clusters in SIT), as members of a team of
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. Knowledge of Control Systems, Mobile Robotics, Automation, Data Science, IoT and Machine Learning, as well as computational learning techniques, with application in the context of manufacturing systems
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and will work with our stakeholders to provide hardware and software support as well as training for desktops, laptops, mobile devices, printers, AV equipment ,IOT devices, and a variety of other
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in R&D projects in Control Systems, Mobile Robotics, Automation, Data Science, IoT and Machine Learning, as well as computational learning techniques, with application in the context of manufacturing
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: Experience with time-series analysis, predictive modeling, and anomaly detection. Familiarity with real-time applications of AI/ML in embedded or IoT devices. Knowledge of cloud-based computing platforms
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digital twin, and artificial intelligence solutions through applied research projects. Desirable Good knowledge in digital supply chain-related technologies such as AI, LLMs, Cyber Security, IoT, 3D