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data Knowledge of statistical methods in the context of biological systems Experience with programming (Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will
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, STATA, SAS or other statistical packages; demonstrated expertise in the analysis of large and complex datasets; excellent written and oral communication skills in both English and Chinese (Cantonese and
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agreement. Duration: 2 years with possibility for extension. Principal supervisor is group leader, Prof Joachim Weischenfeldt, BRIC and Rigshospitalet, joachim.weischenfeldt@bric.ku.dk , Phone: +453545 6040
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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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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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Parkinson’s Disease, or markers of brain structure and functioning, depending on the dataset. To do this, knowledge or willingness to be trained in advanced statistical modelling, ideally with an interest in
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Sustainable Decision Making (Prof. Dr. Clemens Thielen), which is located at the TUM Campus Straubing for Biotechnology and Sustainability (TUMCS) and affiliated with the Department of Mathematics. The expected
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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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, including: Genomic technologies – hands-on experience in long-read sequencing and variant interpretation Bioinformatics – pipeline development, visualisation, and statistical modelling PRS – applying big data