You will work in the Digital Waters (DIWA) Flagship (https://digitalwaters.fi) developing its research and innovation actions in collaboration with various international and national research projects and partners. The work involves the development of digital solutions with focus on data assimilation combining monitoring, modelling and data management in the process of creating a national to international scale hydrologic digital twin. You will also collaborate and assist in doctoral education and research alongside other senior staff and professors. Some teaching and supervision of graduate students may also be considered.
The primary objectives at DIWA are to develop a digital twin platform for water management systems, to explore various use cases related to water, employing physics-based and data-driven approaches to create models for detection, prediction, prescription, and proactive maintenance, and to integrate additional analyses and models into our digital twin services (such as what-if analysis) in the future. The Digital Twin Platform objectives are to allow researchers to share their data and developed models for water resource related use cases in a systematic and accessible manner, and to provide digital twin services for showcasing and technical pilot purposes.
We are looking for a talented researcher with a strong background in earth system science (e.g. hydrology, geosciences, environmental engineering, or a closely related field) with demonstrated expertise in data assimilation methods and their application to hydrologic or Earth system models. You should have experience integrating process-based models with AI or machine learning approaches, proficiency in scientific programming (e.g., Python), geoscientific computing workflows and workflow automation, large data handling, and high-performance computing (HPC). Familiarity with remote sensing products, novel sensor networks, and the development of operational or near-real-time environmental forecasting systems is highly desirable. Ideally you are a clear communicator who can work across disciplinary boundaries, contributing to the design and implementation of scalable, operational data assimilation systems that integrate diverse observational data streams into large-scale hydrosphere models.
Learn more here: https://oulunyliopisto.varbi.com/what:job/jobID:907807/