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skills in R and/or Python and experience in statistical modelling of high-dimensional biological data. Experience working in HPC environments and managing large-scale sequencing datasets is essential
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application in the life sciences, preferably in microbial genomics. Strong programming skills in Python or an equivalent language. Strong communication skills and experience in working in a team. The following
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) studies is expected. Solid skills in at least one programming language (R, Python or Perl) and experience working in Linux and/or high-performance cluster environments. A strong ability to perform
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skills (Matlab, Python, C++) are a significant asset. •Knowledge of artificial intelligence techniques and associated optimization algorithms would be appreciated. •Good knowledge of English (working
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a strong background in statistical analysis and data-driven modeling applied to large astronomical datasets and time-domain surveys. Expertise in programming languages such as Python, R and C
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methodologies: optogenetics, calcium imaging, viral tracing, tissue clearing, murine behavioral phenotyping, machine-learning behavioral analysis Familiarity with programming languages (e.g. R, Python) and an
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. Generating fortran code for VUMAT for Abaqus Explicit. Developping python codes for microstructure generation Finite elements simulations on a cluster. Parameter study and comparison with experimental results
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, health services research, epidemiology, biostatistics, public health, or a related quantitative discipline. Strong quantitative and analytical skills with proficiency in R, Python, Stata, or SAS
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the use of bioinformatics tools for metagenomic and transcriptomic data analysis (e.g., QIIME, DADA2, R, Python). Demonstrated ability to independently design and conduct experiments, analyze data, and
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engineering pipelines. Implement models in Python using frameworks such as PyTorch, TensorFlow. Perform statistical analyses and model benchmarking against existing methods. Collaborate with wet-lab scientists