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Field
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Vacancies PhD position on Dependability Driven on Device Learning Algorithms for Embedded Neuromorphic Architectures Key takeaways Edge devices that can learn autonomously while guaranteeing
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algorithms to detect complex structural variants in humans using long DNA sequencing reads. A structural variant (SV) is a large-scale alteration in the genome that involves rearranged, deleted, or inserted
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motors and braking technology, high-torque density axial flux electrical motors, development of servo controllers and algorithms, and special electrical machines such as superconducting electrical motors
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We are offering a PhD position in the field of algorithmic graph theory. The position is a full-time employment with a competitive monthly salary and full social benefits for up to five years. You
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technologies for medical diagnostics, treatment, and monitoring. Our research activities span computational modeling, algorithm development (using both traditional signal processing and machine learning
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further developed and supplemented by additional image-processing algorithms for studying liquid flow in real time. Development of experimental design and test rigs. Evaluation of accuracy in measurement
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advanced motion planning algorithms with machine learning techniques, such as reinforcement learning, imitation learning, and task generalization. You will focus on designing intelligent robotic systems
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algorithms, focusing on SNN few-shot, synergic (local-global) and asynchronous learning strategies suitable for real-time and embedded systems scenarios. Finally, you will benchmark the developed system
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algorithms for AI agents to learn, apply, and generate justifications in collaborative settings. You will be co-supervised by Davide Dell’Anna (Utrecht University) and Myrthe Tielman (Delft University
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Accountabilities Characterising particles supplied by the project partners using Ocular Systems technology Developing a DEM model for predicting packing density Developing algorithms and methods for processing data