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techniques) at UGent combined with machine learning, deep learning and data fusion modelling to enable development of novel decision support systems for variable rate fertilization and manure application. He
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combined with an interdisciplinary toolbox drawing from ecology, forestry, and climatology. These data will then feed into cutting-edge joint species distribution models to project European forest plant
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conduct stability, reactivity and possible semi-synthesis study of selected bioactive SLs. • You perform a mix of chemical laboratory work and data analysis of natural products. • You contribute to a
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closed-loop control. The technology, developed through detailed computer simulations, will be validated with preclinical experiments. The candidate will be part of a multidisciplinary team working towards
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genomics/transcriptomics and differential expression analysis. You will integrate experimental and genomic data to identify the evolutionary genetic background of climate adaptation in diatoms. Data
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data analysis and biomarker discovery • Collaborate with a multidisciplinary team of researchers and clinicians • Engage with the International PSC Study Group, a global research consortium • Work in
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monitoring of different sources of N fluxes in the system. He/she will need to contribute to the optimization and implementation of advanced sensing platforms for the collection of data on soil N, more
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biodiversity data in tree diversity observatories and experiments across Europe. The goal is to provide empirical understanding of the co-benefits of tree species and mixtures on climate change adaptation and
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responsible for following tasks: • develop a list of soil health assessment indicators, with emphasis on biological indicators • design a detailed sampling & data collection protocol to be used across all
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on real-world medical data, in close collaboration with a team of statisticians and machine learning researchers. Job profile - You have a master's degree and a strong background in statistical data