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of the reports. Such ideas can diverge from a narrow focus on productivity and deal with related topics in Denmark such as competitiveness, internationalization, regional structural change, and innovation. You
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University, preferably 3-6 months at a foreign research institution. Who we are AAU Energy is a dynamic engineering research department in continuous growth and inspiring surroundings. AAU Energy has a very
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The Department of Electronic Systems at The Technical Faculty of IT and Design invites applications for a PhD stipend in the field of Safe Learning Based Control for Autonomous Robots in Dynamic
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forms of knowledge dissemination and complete an external research stay outside of Aalborg University, preferably 3-6 months at a foreign research institution. Who we are AAU Energy is a dynamic
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platforms. Responsibilities and qualifications As a PhD candidate, you will work on developing synergistic workflows to model dynamic, multi-phase systems. Your primary focus will be to overcome
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complementary aspects of how electric fields and electrode-associated niches influence the structure, function, and stability of methanogenic microbial communities in bioelectrochemical reactors. Together
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institution. Who we are AAU Energy is a dynamic and internationally oriented research department at Aalborg University, dedicated to developing clean and sustainable energy systems. Our research spans
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DTU Tenure Track Researcher on Nanoreactors for Operando Visualizations of Nanoparticle Catalysis...
ultrasensitive and quantitative methods for investigating gas-surface interactions on nanoparticles in nanoliter reaction volumes. Relating the three-dimensional surface structure, dynamics and catalytic functions
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and spoken English communication skills Team player with strong collaboration skills Workplace description The successful candidate will join SDU Microelectronics, a dynamic and expanding section
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. To do so, you will combine atomistic simulations (density functional theory and ab-initio molecular dynamics simulations) with new machine learning models to parameterize machine learning force fields