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Are you passionate about advancing sustainable mobility solutions? Do you enjoy working at the intersection of artificial intelligence, optimization, and energy management? We invite applications
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/thesis: Industry-/collaboration PhD student in optimized off-road driving in forests Research subject: Soil science Description: We are looking for an industry/collaboration-based PhD student to develop a
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, optimization) or AI.- Someone who enjoys working in a team, takes initiative, and isn’t afraid to think outside the box.- Someone with excellent grades from BSc and MSc studies, and not afraid of experimental
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distributed MIMO-systems for demanding applications, applicable beyond the XR-application in focus, where high-level models of semiconductor sub-systems is used to optimize an entire system. From a theoretical
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and machine learning to tackle the complexity of force allocation and motion planning under uncertainty and actuator failures. The project combines theoretical research in stochastic optimal control
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of Electrical Engineering . You will be supervised by senior researchers with expertise in robotics, machine learning, automatic control, and optimization. The group leads and participates in numerous
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kind in Sweden and, together with MemLab – the industrial membrane process research and development centre – offers excellent infrastructure for developing and optimizing membrane processes from lab
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tasks include development of adequate single-molecule labeling strategies, optimized use of high-precision MINFLUX microscopy, and establishing methods for data precision analysis. What we offer A
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contribute to the continuous development of the beamline and help users to optimally use the possibilities offered. You will be part of the team developing and operating MicroMAX and work in a group of
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and optimize robotic software systems using Python and C++. Create and manage simulation environments tailored to specific robotic applications. Work with ROS (Robot Operating System) for robot control