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for this position. Applicants should be proficient in R, Python, or equivalent statistical software. Some background knowledge in either (computational) Bayesian methods, or statistical learning for molecular data
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MATLAB or Python) Desired qualifications: The ideal candidate should have: Demonstrated knowledge of fluid dynamics Experience with anisotropic viscous flow Experience with ice texture formation Skills in
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to work independently and collaboratively in multidisciplinary teams Desired qualifications Crispr Crispr / siRNA screens Proficiency in programming (e.g., Python, R) Experience with high-throughput
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) techniques applied to geological materials (e.g., EBSD, FIB-SEM, EDS, STEM imaging) Computational skills (e.g. Matlab, Python) Previous experience, at the PhD level, in one of the following fields: (1
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requirements for admission to the faculty's Doctoral Programme You must have good written and oral English language skills Experience with programming in Python or similar languages
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, biostatistics or similar Experience with handling of large-scale human genotype and registry data (quality control and analysis) Skills in programming and scripting languages (Python/R/Matlab) Fluent oral and
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programme may be given consideration. The suitable candidate must have: Strong programming skills (preferably Python or MATLAB) Experience with using satellite remote sensing data (ideally for polar
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) for general criteria for the position. Preferred selection criteria Background in programming (Matlab, python, …), familiar with Multi-body dynamic tools and good knowledge of statistics will be an advantage
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Penetrating Radar Near Surface Geophysics Operation of planetary science instruments The Post Doc candidate must have experience in Python programming The Post Doc candidate must be eligible to be able to join
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commonly used programming languages in the field (Python, MATLAB, IDL, Fortran, etc). Prior experience working in the field or other high stress environments. Outgoing and eager to work in a team setting