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skills (e.g., programming, statistics) or a willingness to develop them will be considered particularly important for this project. Admission Regulations for Doctoral Studies at Stockholm University. About
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methods experience in the statistical analysis of research results or willingness to acquire such experience willingness to conduct a research stay for several month at another institute very good knowledge
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current wording. 3. In accordance with Article 13º of the RJEC, the competition jury is composed as follows: Prof. Margarida Duarte Cerqueira Martins de Araújo (Assistant Professor and Researcher in charge
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comparative and statistical approaches Exploring evolutionary hypotheses by clustering behavioural traits and mapping them onto phylogenetic trees Collaborating with a multidisciplinary team of biomechanists
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, including: Genomic technologies – hands-on experience in long-read sequencing and variant interpretation Bioinformatics – pipeline development, visualisation, and statistical modelling PRS – applying big data
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Knowledge and experience in the analysis of metagenomics and/or biological high-throughput data Knowledge of statistical methods in the context of biological systems Experience with programming (Python, Perl
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organizing collective behaviour Analysing interspecific variation in swarming behaviour using comparative and statistical approaches Exploring evolutionary hypotheses by clustering behavioural traits and
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, quantification, and data analysis, including statistics. Variations of liquid extraction-based techniques, based on nano-DESI and electrospray ionization, will be developed further and coupled to modern mass
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spectrometry for molecular characterization of biological samples, using tandem mass spectrometry and reactive chemistry, quantification, and data analysis, including statistics. Variations of liquid extraction
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 7 days ago
related to staff position within a Research Infrastructure? No Offer Description The Quantitative Genetics research group is interested in developing statistical genomics toolboxes to decipher the genetic