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analysis: Applying geospatial methods (GIS mapping, geographically weighted regression, spatial clustering) and temporal approaches (time-series analysis, distributed lag models, case-crossover designs
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for NCDs. This will involve: Spatial analysis: Mapping and modelling environmental exposures at fine spatial resolution using GIS tools, geographically weighted regression, and spatial clustering techniques
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grades, awards, or led to scientific publications. Proficiency in Python is required. Experience with additional programming languages, such as MATLAB or C/C++, is considered a plus. Excellent English
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in mechanical engineering, electrical engineering, computer science, AI, or related field, with outstanding study results. DESIRABLE REQUIREMENTS: Programming experience in Python, particular
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machine learning. Strong proficiency with R/Python and machine learning frameworks (e.g., PyTorch, TensorFlow). Prior experience with workflow management tools (e.g., Snakemake, Nextflow). Familiarity with
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strong plus. Experience with Matlab, Python or a similar language and environment is a plus. Depending on the skills of the candidate, training can be arranged in the first year of the PhD. • Good
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of working in a multidisciplinary, international consortium?Are you familiar with Python, MATLAB, or similar tools for data analysis and optimization?Are you eager to contribute to EU-wide goals on energy
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Python or R, and experience in the Linux environment Experience with large-scale data analysis, such as genomics or transcriptomics data Experience with a workflow management system such as Snakemake
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, critically-thinking, self-motivated, flexible, and enjoys working in a team Desirable but not required: Programming experience, preferably in Python or R, and experience in the Linux environment Experience
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internships) in control theory or event-camera sensing is considered a strong asset. Experience with scientific computing in Matlab, Python, or Julia is required. Excellent proficiency in the English language