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non-tenure-track, renewable contract position at the rank of Assistant Professor, with an immediate start date. Exceptional applicants are sought that have a passion for research on Neuromorphic
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the integration of research findings into the broader Sliding Lands project. Key research objectives include: Building a time-series data cube of landsliding across New Zealand using satellite imagery archives
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If you are establishing your career as scientist and want to be at the forefront of sensory research with focus on the use of modern objective and subjective sensory methods, then this position may
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. when do we stop modelling? How do we track / score the quality of the model What is the required level of quality over time How can quality be brought to the required level Can Machine Learning, Large
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description This project addresses the effective design of a military supply logistics network, composed of transportation and communication links such as roads and rail, aerial drone routes, and nodes, such as
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-tracking, pupillometry), cognitive modelling, and regulatory analysis to assess how algorithmic explanations shape human judgement and how existing legal and ethical frameworks align with the evolution
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prediction, signal tracking, fluid dynamics, and space exploration. Advancing Signal Modelling with Physics-Informed Neural Networks This project aims to develop Physics Informed Neural Networks (PINNs
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institutions in five European countries and will be supported by a network of industrial and academic partners. About the position The objective of this project is the development and application of soft sensors
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essential. Project Details This PhD research aims to elevate wind turbine blade technology by advancing owl-wing and other bio-inspired designs for noise reduction and aerodynamic efficiency. Key objectives
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
engineers detect faults earlier, track system degradation, and make better-informed maintenance decisions. But how can we turn this complex information into something reliable, explainable, and actionable