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Summary The Aviation Research Lab is seeking a student to support development and testing of Virtual Reality (VR) simulations for a drone training project. The student will assist faculty
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prediction in a production system context, using various diagnostic tools. Grassland parameters, like pasture condition and grazing intensity, will be characterized using drones and satellites, together
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observed pipe failures, and interpreting indirect observations from remote sensing platforms such as drones and satellites. Another challenge is linking these observations to physical processes affecting
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mapping, GIS and drone photogrammetry for 3D results of specific case studies. This role directly contributes to the development of a high-resolution comparative spatial atlas of long-term refugee camps by
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artificial intelligence and control systems. In this project, you will design new algorithms for semantic communication between the cloud and autonomous systems—technology that can transform how robots, drones
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ARDITI - Agência Regional para o Desenvolvimento da Investigação Tecnologia e Inovação | Portugal | about 1 month ago
(European eel) on Madeira Island. At the technological level, the goal is to create, test and refine a low-cost prototype of a VHF radio receiving system coupled to aerial drones. This system will be compared
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dynamics and behaviour, e.g. aerial/drone surveys, line transects, camera surveillance and photo-ID. Experience with Bayesian statistical modelling Proven ability to handle large ecological datasets and
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Unmanned Aerial Systems (UAS) to deliver high‑quality instruction in our Part 107-focused curriculum. The successful candidate will design and teach courses related to drone operations, FAA regulations
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11.02.2026, Academic staff The TUM Chair of Aircraft Design is looking for a doctoral researcher in the field of Ground and Flight Testing of eVTOL & fixed wing drones/UAVs. The research is targeted
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, DeepFields (using drones, airborne optical sectioning (AOS) -a unique synthetic aperture sensing technique developed by JKU-, and machine learning for harvest and damage estimation in agriculture), in