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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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high-quality research on interpretable and learning-based stochastic optimal control for over-actuated electric vehicles, with a focus on ensuring robustness and fail-safe operation. You will: - Develop
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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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/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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This PhD position at Chalmers University of Technology offers an exciting opportunity to work in an interdisciplinary environment and receive training and support in materials design and synthesis
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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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into two main areas: (1) material development and characterization to ensure optimal sensing and mechanical performance, and (2) structural evaluation of SS-FRCMs under environmental stressors such as freeze
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can identify an optimal evaluation scheme for that particular instance of the expression from the compiled e-graph. The project extends our previous work on linear algebra compilers: https
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construction industry. We run the Robotic Fabrication Lab – a unique experimental space equipped with three customizable industrial robots, where we explore cutting-edge architectural themes, including digital
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at the single-cell level, using tools from optimal transport, mathematical optimization, and machine learning. In addition to method development, the work includes applying and benchmarking algorithms on both