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Field
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bioinformatics with: Proven affinity for cancer and/or immunology research; Experience with genomic analysis from high-throughput sequencing data (R, Phython,Matlab) and affinity with statistics and machine
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strong background and interest in conservation. The ideal candidate is able to work independently, and has proven experience with managing running field experiments, statistical data analyses, and
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, and with a strong background and interest in conservation. The ideal candidate is able to work independently, and has proven experience with managing running field experiments, statistical data analyses
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modeling, multi-variate statistics, and Threshold Indicator Taxa Analysis. Consistency in identified tipping points and affiliated uncertainties using the different statistical approaches and biodiversity
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or agile approaches. You are confident in qualitative and quantitative research methods, data analysis techniques and inferential statistics. It is an advantage if you are familiar with the process of
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. Experience in advanced data analyses and statistical inferences; Hands-on experience in setting up rodent experimental set-ups Experience with in vivo brain activity measurements in freely moving animals
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the research; Collecting postmortem MRI scans; Performing MRI data analysis and developing pipelines; Statistically analysing of MRI and neuropathology data; Writing scientific articles based on the results
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,Matlab) and affinity with statistics and machine learning will be a plus; The ability to work independently, but can thrive in a multi-disciplinary academic environment and communicate fluently in English
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applicant has completed a PhD and has a background in environmental humanities, social sciences, ecology, or any other relevant research field, alongside proficiency in statistical and quantitative
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cognition, and particularly biological mechanisms. Experience in analyzing large datasets with repeated assessments. Above average statistical skills, preferably R. Excellent writing and presenting skills