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Research

Our research From economics, statistics and economic history to business administration, research policy, informatics and business law. We are more than a business school. Research areas Highlights from all of our departments Our upcoming research related events Our Research Centres Research areasFind a researcherLUSEM in the mediaDoctoral studies Research news articles New research findings, upco

https://www.lusem.lu.se/research - 2025-07-25

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Välkommen till Institutionen för strategisk kommunikation Möt Sveriges och en av Europas största enheter för forskning och utbildning inom strategisk kommunikation Utbildning På Institutionen för strategisk kommunikation utbildas blivande kommunikatörer, marknadskommunikatörer, PR-konsulter och kommunikationsstrateger. Våra utbildningar är unika i Sverige, och i flera fall även i Europa. Ny studen

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About NanoLund NanoLund is Lund University’s Center for Nanoscience and Sweden’s largest research environment in this area. Research NanoLund is a cross-disciplinary research organisation specializing in various areas of nanoscience. Lund Nano Lab An open-access facility for micro- and nanofabrication, offering cutting-edge equipment and expertise. For NanoLundians Activities and resources for our

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Leading environmental and climate research We conduct cutting-edge and renowned research on the environment and climate. Education in environmental science CEC offers undergraduate and postgraduate education in environmental science. Previous Next Play Pause Our research Projects and networks Collaboration Our partners PhD studies Collaborative platforms Join our team! Open positions Calendar Link

https://www.cec.lu.se/home - 2025-07-25

Leveraging axis-aligned subspaces for high-dimensional Bayesian optimization with group testing

Bayesian optimization (BO ) is an effective method for optimizing expensive-to-evaluate black-box functions. While high-dimensional problems can be particularly challenging, due to the multitude of parameter choices and the potentially high number of data points required to fit the model, this limitation can be addressed if the problem satisfies simplifying assumptions. Axis-aligned subspace appro