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Field
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(R, Python, etc.), and working knowledge of data management protocols. Experience with exposome or environmental data, multiomics integration, and exposure-wide association studies (ExWAS) and GIS
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Programming in Python, R, SQL, Java, or similar languages Spatial and spatiotemporal methods when relevant, including geo-visualization, spatial optimization, and spatially explicit modelling for sustainability
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software (e.g. ArcGIS, QGIS) and coding environments (e.g. Python or R), collaborating across LUMHR themes, and supporting interdisciplinary research activity. Teaching support may be required, up to a
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accepted for publication. Expected skills Proficiency in programming (R, Fortran, Python) and the ability to learn new programming languages, develop code, and analyze data. Knowledge of GIS tools and
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geospatial data processing and python programming. Candidates should also have knowledge of optical, lidar, and ground penetrating radar sensing systems and understanding of pavement structures and condition
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Conduct in-vitro and ex-vivo experiments in simulated GI environments Develop and integrate biocompatible sensing modules for IBD biomarker detection Test targeted drug delivery mechanisms Perform data
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modeling, for example, with FLAMMAP or FSim, Experience in analyzing large spatial and tabular datasets, Experience in GIS using ArcPro or R Terra, and; Experience coding in R and/or Python. Stipend
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such as Python, C/C++, or Java. Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and geospatial libraries (e.g., GDAL, GeoPandas). Experience managing or supervising
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, and interprets cancer surveillance, population, geographic, environmental, and health services catchment area data using appropriate data management, statistical, GIS, and other programming software
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and software development emphasis. Demonstrated experience with geospatial analysis using Python in a production or research to production environment leveraging common geospatial libraries (e.g