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by relevant publications and research projects. Technical Skills: Proficiency in modeling and simulation methods for energy networks. Experience with energy network analysis tools (e.g., MATLAB, Python
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(e.g., Bioconductor, Galaxy, KEGG, Reactome, STRING). Proficiency in Python, R, and Unix/Linux-based environments for high-performance data analysis. Knowledge of biological network inference, causal
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CBS - Postdoctoral Position: Artificial Intelligence Applied to Metabolomics for Health Applications
classifiers). Proficiency in programming languages such as Python, R, and experience with bioinformatics pipelines. A proven track record of scientific publications in metabolomics, AI, or computational biology
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, Organization of field trips, data collection and lab work, Spectral data analysis, data processing, and model development, ‘R’ or Python programming, Co-supervise PhD and undergraduate students. Be willing to be
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. Proficiency in programming MATLAB and Python for data analysis and algorithm development. Knowledge of data assimilation techniques is a valuable added. Excellent communication skills and ability to work
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hydrological modeling at catchment scale, and familiarity with modeling tools. Proficient in statistical analysis and programming languages such as Python, R, or MATLAB. Strong written and verbal communication
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bioinformatics toolkits. Practical experience in analyzing large datasets. Programming skills (Python, Perl or R). Demonstrated publication record in well-ranked journals. Ability to generate new ideas, links, and
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-economic issues. • Proficiency in urban modeling tools such as MATLAB, Python (especially libraries like Pandas, NumPy, SciPy, GeoPandas, etc.), and R. • Advanced skills in predictive modeling and
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-principles and data-driven models), simulation and optimization methods. Programming languages, i.e. Python, Matlab, ... Level of experience evidenced by publications in peer-reviewed journals. Fluent in
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on analyzing satellite-derived data (Sentinel-3, Landsat) to monitor coastal ecosystem changes. Develop workflows in R, Python, or MATLAB to process and analyze ocean color data, with a focus on chlorophyll-a