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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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Are you curious about harnessing the unique flavour properties of micro- and macroalgae? In this project at Chalmers University of Technology , challenges and possibilities with algae as food ingredients will be explored, maximizing the acceptance of algae-containing foods on the European...
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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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. A principal focus of our research is the development of technology components for 6G wireless networks. Specific topics include the design and optimization of distributed and massive MIMO for high
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genetic data (e.g. from genotyping). You will implement and evaluate selection methods: Test and optimize new methods for precision breeding in practical or simulated improvement programs. You will work
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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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particular focus on the effects of boundary layer ingestion. The work also includes: Whole-aircraft noise prediction Flight trajectory optimization Environmental and societal impact assessment of future air
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unwanted effects from so-called concomitant gradient fields. You will also develop software to facilitate experimental design. This includes numerical optimization of the instructions that control the MRI