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Field
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machine learning—building, curating, and governing the data that directly determines whether AI/ML models succeed in operationally relevant environments. This position can be filled in State College, PA
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modeling or geospatial/temporal data analysis Causal inference ● Strong programming skills in Python and experience with PyTorch, required to have experience developing code with a team through collaborative
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sequencing (NGS) and imaging data · Expertise with statistical modelling and the design of geospatial statistical software · Excellent communicator and professionalism · Experience
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Experience in working with large geospatial datasets (e.g., ERA5, CMIP6, Sentinel, MODIS) Working with complex models and high performance computing, ideally dynamic global vegetation models (DGVMs
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raster geospatial data related to soil degradation, preferably in areas at risk of desertification; (ii) Proven experience, as an advanced user, with a GIS (e.g., ArcGIS or QGIS), with the R project
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chemical transport model (CTM). These geospatial inputs and information will be used to fine-tune a NASA foundation model (Prithvi WxC) to emulate CTM processes, and to predict ground-level air quality data
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hands-on experience and skill in using novel spatial and geospatial data sources, advanced analytics, and computational modeling. Areas of focus through which qualitative and quantitative data can be
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modelling, geospatial data science, generative modelling, mobility data analysis, or transport simulation, especially when combined with knowledge of complex systems, network science, resilience theory, urban
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Job Description Model Development: • Develop and train surrogate ML models (e.g., neural networks, Gaussian processes, gradient boosting) to emulate computationally intensive building and urban
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at the time of appointment; Research experience in agricultural remote sensing, crop and pasture modeling, or agricultural environment monitoring and prediction; Knowledge of remote sensing, geospatial