223 massey-university-flying positions at Delft University of Technology in Netherlands
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the project, a PhD position is now available on “Neuromorphic Flight Control and Obstacle Avoidance for Autonomous Drones”. The topic of the PhD is to develop neuromorphic attitude estimation and control, ego
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Challenge: Develop a climate model that enables climate optimized flight trajectories to be achieved at robust manner. Change: Improve the current climate model with broadening their geographical
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of the Venus atmosphere. In close collaboration with both instrument teams and ESA specialists, you will use the code in combination with instrument models to inform the pre-flight calibration processes and to
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guidance approach that will enable even very small flying robots to achieve similar navigation feats. The research will involve developing navigation strategies and spiking neural network learning setups
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shall focus on state-of-the-art machine learning techniques, that can find patterns within vast amounts of data, to refine the estimation of flight trajectories and fuel consumption. Additionally, you
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. The first step is to compute electromagnetic wave field propagation operators from GNSS-R data recorded at multiple receivers. Formation flight nano-satellites will provide the opportunity to generate a dense
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at different locations and at different times. The end-goal is ML-driven detection of complex activities, by designing neural networks for evolving spatio-temporal graphs to integrate multi-source on-the-fly
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, on ice slabs and biochemical components. Finally, if time permits it, you will be involved in the design of a novel ellipsometer dedicated to surface characterization, flying on drone or space-borne. You
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constraints that autonomous robots need to adhere to, synthesize verification algorithms that ensure safe planning and control, and adapt models on the fly during the robot’s operation to account for
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. You will implement reinforcement learning methods and/or graph based neural-networks incorporating process, thermal and optical data to predict the local and global material properties on-the-fly