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Essential Application Experience creating data dashboards and visualisation tools for engineering applications. Essential Application/Interview Practical experience in sensor networking and IoT integration
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: Collaborative Localization and Sensor Integration This PhD position will focus on developing and integrating methods for collaborative localization and sensor fusion in the ABBA USV system. The goal is to enable
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, Physics , quant-ph , Quantum Science + Quantum Information Science + Quantum Optics + Theoretical Physics , Quantum Sensors , Theoretical Particle Physics , Theoretical Physics, HEP-Phenomenology (hep-ph
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quantum networks, where one bit of information is encoded in the quantum state of a single photon. You will be part of a team of 10-12 people between senior staff, PostDocs and PhD/Master students
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, pulsed power, or sensor development, looking to apply your background to next generation power electronic conversion and related measurement in pursuit of Carbon Net Zero? We are seeking motivated
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Mobility (IAM), considering ecological, economic, technological, and sociological factors. The RTG's structured PhD program aims to train young researchers in highly automated, networked mobility, featuring
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The Brandenburg University of Technology Cottbus-Senftenberg (BTU) bundles top-level research and transfer at an international level, thereby creating an interdisciplinary innovation network and an excellent
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Engineering Director Our website https://sheffield.ac.uk/eee For informal enquiries about this job, contact Professor Chee Hing Tan, Professor of Opto-Electronic Sensors: on c.h.tan@sheffield.ac.uk or 0114 222
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simulation, including O/D modeling, multimodal network modeling, agent-based or behavioral modeling Large-scale computing, cloud-native analytics workflows, and data engineering for mobility platforms AI/ML
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join our research team focused on the development of innovative and sustainable sensor systems for industrial applications. This position offers an exciting opportunity to contribute to cutting-edge