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theoretical problems. Good computational/programming skills in Matlab or Python High proficiency in written and spoken English Research at our Faculty The Faculty of Science is a world-class faculty where staff
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. Research methods include computational modelling, brain imaging (fMRI), machine learning, behavioural methods, and other techniques. Virtually everything we sense, think and do is uncertain. For instance
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The PhD position is embedded in the research programme of Economics, Econometrics & Finance of FEB’s Research Institute. The project will be supervised by dr. J.A.M. de Grefte, prof. dr. B.P. de Bruin and
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for your job with EUR? Then you may be eligible for the 30%-ruling if you meet the requirements of the Belastingdienst (Dutch tax agency), and with our Dual Career Programme we will also help your partner
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(spoken and written). Preferred qualifications Prior experience with 3D cell culture, organoids, CRISPR-Cas9, or imaging-based phenotyping. Familiarity with transcriptomics or basic computational biology (R
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-year PhD program, you will join a collaborative research team applying cutting-edge methods from Experimental/Behavioral Economics alongside modern macroeconomic modelling techniques (e.g. DSGE). You'll
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. They can be constrained by either compute power or memory bandwidth. This information can be used to calculate the theoretical maximum energy efficiency of an algorithm that is run on an architecture
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- Wageningen University & Research ) and Dr. Karen Kloth ( Karen Kloth - Wageningen University & Research ). Your project is part of the MiCRop Consortium programme on microbial imprinting of crop resilience
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for Energy Business and Economics Research (CEnBER - https://www.rug.nl/cenber/ ) and in the research programme Economics, Econometrics & Finance of FEB’s Research Institute. The project will be supervised by
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://research.wur.nl/en/persons/rieta-gols ) and Dr. Karen Kloth (https://research.wur.nl/en/persons/karen-kloth). Your project is embedded in the MiCRop Consortium programme on microbial imprinting of crop resilience