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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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of four years of full-time doctoral education is required. We are looking for an ambitious doctoral candidate that will work on advancing our understanding of how DNA topology and topoisomerase activity is
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topological magnetic structures in optical cavities. Work duties The main duty involved in this position is to conduct research, and the co-supervision of master's theses and doctoral students. Teaching may
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) biological knowledge about GRNs from bioinformatics and system biology, (b) graph theory and topological data analysis for network modeling from mathematics, and (c) robust machine learning (ML) and GenAI from
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to optimally prepare it for use in AI-driven systems and solutions. About us The Department of Computer Science and Engineering at Chalmers and University of Gothenburg in Sweden with approximately 300
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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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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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successfully conducting research as well as postgraduate and undergraduate education within areas such as autonomous systems, complex networks, data-driven modeling, learning control, optimization, and sensor