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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The Department of Mathematics and Mathematical Statistics is opening a PhD position in spatial statistics
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. The Department of Mathematics and Mathematical Statistics is opening a PhD position in spatial statistics
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methods for single-cell data analysis (tools developed by the team : https://github.com/cantinilab ). Single-cell high-throughput sequencing, extracting huge amounts molecular data from a cell, is creating
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the occupational risks faced by inhabitants of the Roman Empire influenced their choice of preferred cults. The main methods used in the project include spatial analysis, predictive modelling, statistics, and the
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more about our benefits and what it is like to work at SLU at https://www.slu.se/en/about-slu/work-at-slu/ Development of statistical methods for estimating plant population size and change Mathematical
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the Department of Human Geography and Spatial Planning (max. 10% of the appointment). Where to apply Website https://www.academictransfer.com/en/jobs/358070/phd-the-urban-ocean-nexus-towar… Requirements Specific
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particular emphasis on its contribution to biodiversity conservation and carbon sequestration under ecological restoration scenarios. The project will integrate spatial analysis, connectivity modelling, and
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SD-26045-RESEARCHER IN ADVANCED PLASMA-ASSISTED DEPOSITION PROCESS DEVELOPMENT FOR CATALYTIC THIN...
in their decisions and businesses in their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? In the framework of a bilateral project
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics