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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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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
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that enable energy-intensive industries to plan and optimize their production based on energy demand, power consumption, and sustainability. The current PhD position focuses on analyzing how digitalization and
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on building the next generation of quantum processors based on superconducting circuits. To achieve this ambitiuous goal, we have a variety of projects related to: Development and optimization of nano
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on designing and synthesizing advanced materials for next-generation batteries—such as solid-state, multivalent-ion, or aqueous rechargeable systems—while collaborating with the research team to optimize