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Field
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quantitative methods, with a strong emphasis on GIS and spatial analysis. A central part of the work will be to trace the effectiveness of colonial health interventions across time and space, linking observed
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, genetics, or related field • Expertise in genomic data analysis, particularly single-cell RNA-sequencing and/or alternative splicing • Strong background in bioinformatics and programming skills (e.g., Python
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programming skills (e.g., Python) Previous experience with analyzing sequencing data Excellent communication abilities, and a collaborative mindset Meritorious for the position: Demonstrated work on developing
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sciences, bedrock geology, paleontology, physical geography, biodiversity and ecosystem science, remote sensing, Geographic Information Science (GIS), and computational science for health and environment
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manufacturing. It is meritorious to have previous experience in data analysis and processing with Python (or similar), preferably including documented experience with machine learning tools. It is meritorious
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analysis, for example with Python or Matlab. Consideration will also be given to how the applicant’s experience and skills complement and strengthen ongoing research within the department, and how they stand
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diseases. Programming knowledge in R or Python is a requirement. The applicant should also have experience in machine learning. Experience in analyzing multiple MRI modalities such as sMRI, DTI and fMRI is
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as evidenced by strong scientific publications and track record relative to career stage. Strong programming skills (Python or R) and familiarity with high-performance computing Exceptional
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with Java or Python are additional merits. The successful candidate should be creative, have the ability to both co-operate and independently work with research questions. Proficiency in written and
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stage. Strong programming skills (Python or R) and familiarity with high-performance computing Exceptional collaborative abilities Preferred qualifications A doctoral degree or an equivalent foreign