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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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exists for researchers to design and improve animal tests. These limitations hinder the development of optimal experiments and incur cruel animal suffering and killing.The position is two years and you
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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
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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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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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description and working tasks Linear algebra expressions are evaluated in an efficient and robust way by mapping them to a carefully chosen sequence of calls to optimized functions as offered by libraries