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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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(lth.se). We invite applications for one to two PhD positions dedicated to developing methodologies for the automated analysis and design of first-order optimization algorithms. Such algorithms form
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students by the autumn, in addition to three faculty and two postdocs. Our group's research spans various facets of complexity theory, encompassing both combinatorial and logical perspectives. Key areas
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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