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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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of multivalent nanoparticle vaccines. The team was recently awarded an ERC Advanced Grant to determine the optimal combination of epitopes that elicits the highest level of protection. Within
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Advanced Grant to determine the optimal combination of epitopes that elicits the highest level of protection. Within the research group, we value a positive work environment built on respect and
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. Our research integrates expertise from machine learning, optimization, control theory, and network science, spanning diverse application domains such as energy systems, biomedical systems, material
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. Our research integrates expertise from machine learning, optimization, control theory, and network science, spanning diverse application domains such as energy systems, biomedical systems, material
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another way. The candidate must possess experience with the following technologies: - CUT&RUN - qRT-PCR and - Extensive DNA cloning technologies - Experience in Codon optimization from Dictyostelium
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the optimal design of social support systems. The PhD position primarily concerns the part of the program that studies how AI changes the organization of work and employees. The program currently includes 12
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social marketing within technology and innovation-intensive activities to automation and optimization, to machine design, production and production systems. The Division of Industrial Engineering and
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learning, and combinatorial optimization. Doctoral students are expected to be able to publish high-quality papers and develop research prototypes. After the qualification requirements, great emphasis will