Anthropogenic and Natural Emissions, Air Quality
hace 2 meses
**Context And Mission
In this ambitious and potentially rewarding endeavor, we need a variety of postdoctoral researchers, researchers and research engineers in atmospheric composition, climate, machine learning/artificial intelligence and computer science.
The applicants for postdoctoral and research positions would ideally have interest in at least one of the following topics:
- Development of the BSC emission modeling framework (HERMESv3).
- Development of a near-real-time emission monitoring system for Spain.
- Development of satellite-based emission plume detection and quantification methods to estimate and monitor emissions from urban and industrial hotspots.
- Understanding of multi-phase organic chemistry and development of flexible schemes in atmospheric models.
- Understanding of aerosol microphysics and development of related parameterizations in atmospheric models.
- Design and development of machine learning/artificial intelligence models to emulate air quality models or components (e.g. chemical mechanism) and improve emission inversion estimates.
- Understanding the impact of vegetation fire emissions on ozone and particulate matter.
- Characterization of dust sources and mineralogy based on space-borne spectroscopy (e.g. EMIT, EnMAP).
- Improving our understanding desert dust emission and its variability and trends.
- Exploitation of in-situ and/or remote sensing observational data for atmospheric composition modelling, data assimilation, evaluation, and emission inversion.
The applicants for research engineer positions would ideally have interest in at least one of the following topics:
- Python developments for emission modeling and evaluation.
- Atmospheric composition/emission modeling performance and modularization.
- GPU porting/offloading for atmospheric composition/emission modeling.
- Workflow developments for operational forecasts and data assimilation.
**Requirements
Education
- Postdoctoral researcher and researchers: A Ph.D. degree in environmental engineering, atmospheric chemistry, physics, climate, data science, remote sensing, computer science or similar. Research Engineers: A Bachelor or Master degree in environmental engineering, atmospheric chemistry, physics, data science, remote sensing, computer science, telecommunications or similar.
Essential Knowledge and Professional ExperiencePostdoctoral researcher and researchers:
- Good computing skills in high-level computer languages (FORTRAN, C, Python or R) and experience with UNIX/Linux environments.
- Demonstrated scientific expertise, including but not limited to a record of scholarly publications.
Previous experience or ambition in at least one or more of the following fields:
- Atmospheric emissions and characterization of emission sources.
- Satellite data processing methods, including those based on machine learning.
- Development of bottom-up emission inventories, emission inventory intercomparison and evaluation of emission estimates by means of air quality modelling.
- Design and development of machine-learning/artificial intelligence models.
- Development of air quality modeling and strong skills in model-data integration.
- Aerosol microphysics and/or atmospheric chemistry modeling.
- Spaceborne spectroscopy data analysis.
- Earth system modelling and/or atmospheric modelling and/or mineral dust modeling.
- Processing of L2 satellite products (aerosols and/or trace gases).
- Observations quality control and statistical analysis.
- Experience in data assimilation and/or inverse problems in geophysics.
- Experience in atmospheric composition and/or NWP modelling.
- Using deep-learning frameworks.
Research Engineers:
- Excellent computing skills and experience with UNIX/LINUX environments.
- Experience managing collaborative projects with Git or similar software version control.
- Experience with coding and documentation best practices and standards.
Previous experience or ambition in at least two or more of the following fields:
- Emission inventory developments.
- GIS data and tools.
- Numerical models of the scientific area of the call or related.
- Development and deployment of complex workflows on HPC.
- Computationally demanding models.
- General-purpose, compiled and scripting languages (Bash).
- Programming in Python and/or R.
- Scientific Python packages (Python Numpy, Scipy,).
- Programming in high-level computer languages (especially FORTRAN or C/C++).
- Programming in computer languages such as CUDA or OpenACC.
- Knowledge in GPU porting from CPU codes.
- Knowledge using GPUs/TPUs with at least one deep learning framework (TensorFlow, Pytorch or similar).
- Knowledge in running and optimizing scientific codes on large HPC systems.
- Knowledge in HPC architecture and parallel programming (MPI, OpenMP).
- Understanding of HPC computer architecture issues, including CPU, accelerators, memory, interconnect, parallel I/O, and computational performance in general.
Additional Knowledge and Profes
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