176 structures-"https:" "https:" "https:" "https:" "University of Minho" PhD positions in Norway
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: Digital twin development for heritage risk preparedness AI-based predictive monitoring (humidity, flooding, structural stress) Integration of GIS/BIM, LiDAR and IoT telemetry Federated data architecture and
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team Work in a structured way, set goals and make plans to achieve them Present and discuss your research with other professionals Get involved and contribute constructively with feedback Show enthusiasm
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of the PhD project is to develop an effective control structure and real-time optimization scheme for a novel continuous bioprocess. The control and real-time optimization will then be validated in experiments
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characteristics Highly motivated for applied and collaborative research. Excellent communication and teamwork skills. Self-driven, structured, and reliable. Innovative mindset and ability to work across disciplines
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10th April 2026 Languages English English English The Department of Geosciences (IGV) has a vacancy for a PhD Candidate within Rock Engineering for New Hydropower Plants - Design and Construction
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of the position Numerical simulations of nonlinear photonic waveguides and devices Photonic design of hybrid integrated laser structures for manufacturing at NTNU or in collaboration with external foundries
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transactions; Ship design and construction; Voyage planning and optimization; Condition monitoring and maintenance; Crew training; Maritime traffic and ship surveillance; Decarbonization and energy management
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, the MSCA Doctoral Network project SAILING (https://Secure AI and Digital Twin Empowered Smart Internet-of-Energy ) aims to establish a multidisciplinary network of leading research groups and industrial
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possibility of extension) Start date: August 2026 at latest The students will be enrolled in the structured PhD programme in Computer Science at Sapienza University of Rome, Italy: https://www.uniroma1.it/en
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, and reinforcement learning for adaptive decision-making. A key aim is to connect wireless phenomena to learning robustness by combining physical-layer signal structure and signal-processing insights