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using online testing tools. Analyze complex multimodal neuroimaging data using advanced processing software (e.g., FreeSurfer, FSL) and statistical methods. Prepare scientific manuscripts for publication
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specialization in forest/plant ecology, tree physiology, tree - microbial interactions). Familiarity with molecular ecology, meta-barcoding and bio-informatics is considered a merit. Knowledge of statistical
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, engineering physics, biomedicine, or similar Documented skills in data-driven analysis (machine learning using python with TensorFlow, PyTorch, or similar) and computational statistics Specific knowledge of big
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physics, electrodynamics, statistical physics, and fluid dynamics). To succeed as a PhD student, you must be creative and devoted to work. You must also have good interpersonal skills, be resourceful, and
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an asset. Additional qualifications Working knowledge in statistics and infection biology is highly appreciated. Part of the DDLS program, to be employed as a PhD student, the applicant must be
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vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid
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techniques coupled to high resolving mass spectrometry for molecular characterization, using tandem mass spectrometry and reactive chemistry, quantification, and data analysis, including statistics. Variations
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. Candidates are further expected to have experience in processing and analyzing high-throughput genomic sequencing data and in statistical analysis. Previous experience with Drosophila melanogaster or other
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Lepidoptera and plant ecology Statistics and programming (e.g. in R) The application should consist of the following (all files in PDF-format): Curriculum vitae including publication list, Master [alternatively
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Lepidoptera and plant ecology Statistics and programming (e.g. in R) The application should consist of the following (all files in PDF-format): Curriculum vitae including publication list, Master [alternatively