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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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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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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
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macromolecular modeling: Investigate electronic, optical, molecular, and transport properties of soft materials, including conductive and semiconductive polymers, biopolymers, and macromolecules, to optimize
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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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part of the MARTINA project and will explore the application of co-design optimized machine learning and neuromorphic solutions for applications that are challenging to address using conventional