Postdoc in Peatland Hydrology with Focus on Groundwater and Machine Learning

Description

Offer Description: In this Postdoc position, you will investigate groundwater dynamics in peatlands and develop cutting-edge tools to model these dynamics. Your research on peatland hydrology will help inform future management strategies that support climate resilience, biodiversity protection, and sustainable land use. The position is primarily within the Horizon Europe PEAT-GENIE project . PEAT-GENIE will develop a user-friendly, Digital Twin platform for monitoring, restoring, and managing peatlands across Europe. The project brings together 20 partners from 10 European countries, offering a highly international and interdisciplinary research environment. You will actively interact with the project partners to link groundwater with greenhouse gas emissions, biodiversity, and land use processes within the peatland scope. The position is for 2.5 years, starting February 1, 2027. The work location is GEUS in Copenhagen. About the position The focus of the position is on developing new data-driven approaches to understand and predict groundwater dynamics in peatlands and their response to climate change and land restoration. The specific objectives of the Postdoc project are to: Develop machine learning models for predicting groundwater dynamics in peatlands across space and time. Develop an assessment tool for evaluating the impacts of climate change and peatland restoration on groundwater dynamics through linking numerical hydrological models with machine learning. The project combines hydrological modelling, remote sensing, machine learning, and the analysis of environmental data. You will have the opportunity to broaden your expertise by working across these disciplines and by collaborating closely with researchers from different backgrounds within PEAT-GENIE. Your research findings are expected to be published in leading international peer-reviewed journals. You will also contribute to the international activities of PEAT-GENIE by participating in project meetings, workshops, and international conferences, and by collaborating with project partners across Europe. PEAT-GENIE offers: An exciting research project within Horizon Europe including international collaboration with leading European research institutions Excellent opportunities for scientific publication and career development A highly interdisciplinary research environment spanning peatland ecology, hydrology, remote sensing, biogeochemistry, computer science, and stakeholder engagement The opportunity to develop expertise in emerging applications of AI and machine learning for environmental research Requirements: Research Field Geosciences Education Level PhD or equivalent Skills/Qualifications You are expected to hold a PhD in geosciences, environmental engineering, hydrology, environmental science, or a related field. The appointment will be based on your scientific achievements, academic record, and relevant professional experience. Special emphasis will be placed on experience in one or more of the following areas: Process understanding at the intersection of catchment hydrology, groundwater, and peatland ecosystems Numerical hydrological modelling Groundwater hydrology Machine learning and data-driven modelling Experience with geographical information systems (GIS), satellite remote sensing, Python programming, and the processing and analysis of large environmental datasets will be an asset. Previous experience disseminating scientific results through peer-reviewed publications and international conferences will also be positively evaluated. As the project is highly interdisciplinary and collaborative, particular emphasis will be placed on your ability to work across disciplinary boundaries, engage with colleagues from different backgrounds, and contribute actively to a collaborative research environment. Personal competencies include being structured, independent, responsible, proactive, and inquisitive in a research context, as well as the ability to embr

Sectors

Source

Source: euraxess.ec.europa.eu