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the theory of optimization algorithms and high-dimensional statistics to address some of the most fundamental questions in ML such as the behavior of neural networks. The environment of this project is highly
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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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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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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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evaluation frameworks and/or the development of energy system optimization models. The research is applied and closely linked to industrial interests and needs. About the research Our research aims to provide
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mathematical statistics (University of Gothenburg / Chalmers University of Technology) Prof. Mats Nilsson, pioneer in spatial genomics (SciLifeLab & Stockholm University) Integration into the national DDLS
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departmental duties, up to a maximum of 20% of full-time. Your qualifications You have a Master’s degree in electrical engineering, engineering physics, computer science, applied mathematics or have completed
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, engineering physics, computer science, applied mathematics or have completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses within the topics mentioned above
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extensive expertise in technology and systems for sustainable production of food and bioenergy, including optimal nutrition circuits and logistics systems. Within the field of methodologies, we have extensive
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approach that integrates wireless communication, computer vision, and machine learning to optimize PC transmission from sensors to an edge server for remote registration. The research is funded by Wallenberg