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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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. Our research integrates expertise from machine learning, optimization, control theory, and network science, spanning diverse application domains such as energy systems, biomedical systems, material
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the optimal design of social support systems. The PhD position primarily concerns the part of the program that studies how AI changes the organization of work and employees. The program currently includes 12
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social marketing within technology and innovation-intensive activities to automation and optimization, to machine design, production and production systems. The Division of Industrial Engineering and
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from 3D-optimized end-walls provided by additive manufacturing (AM). The project outcome will solve the urgent need for CO2 reduction from air traffic. Research environment You will join the Fluid
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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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application no later than August 1, 2025. Project description Linear algebra expressions are evaluated in an efficient and robust way by mapping them to a carefully chosen sequence of calls to optimized
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, localization, and sensing, with a focus on developing next-generation multiple-antenna systems while optimizing overall system performance. As a doctoral student, you devote most of your time to doctoral studies