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computational chemistry or physics will be preferred, but candidates with a solid background in statistics, computer science, and/or mathematics are also encouraged to apply. Programming skills (e.g., Python
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(e.g., based on physiological signals or direct inputs from occupants) and developing algorithms, including machine learning methods. The work will include statistical modelling, data-driven modelling
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analytical skills in model evaluation Experience in advanced statistical analysis of results Experience with crop model calibration and evaluation, such as DSSAT, APSIM, or equivalent Insight into analyzing
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analytical skills in model evaluation Experience in advanced statistical analysis of results Experience with crop model calibration and evaluation, such as DSSAT, APSIM, or equivalent Insight into analyzing
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teaching assistant. The Department of Mathematical Sciences has strong research groups in many areas of mathematics, and has an active postdoc and visitors' programme; we refer to our homepage for more
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or laboratory analyses. Familiarity with statistical analyses and data integration across multiple sources. Collaborative skills and ability to demonstrate commitment in teams Motivation to pursue a scientific
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or laboratory analyses. Familiarity with statistical analyses and data integration across multiple sources. Collaborative skills and ability to demonstrate commitment in teams Motivation to pursue a scientific
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of metabarcoding data, plant metabolomics or transcriptomics, multivariate statistical analyses or soil microbiology Further, we will prefer candidates with some of the following qualifications: Teaching and
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of metabarcoding data, plant metabolomics or transcriptomics, multivariate statistical analyses or soil microbiology Further, we will prefer candidates with some of the following qualifications: Teaching and
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of supervision at Bachelor’s and Master's degree level. We expect you to have experience with human electrophysiology (i.e. MEG and EEG, in particular hyper scanning), expertise in advanced statistical analysis