PhD Student in Water Resources Engineering with a Focus on Remote Sensing and AI

Lund University was founded in 1666 and is consistently ranked as one of the world’s leading universities. There are approximately 47,000 students and more than 8,800 employees in Lund, Helsingborg, and Malmö. We unite in our efforts to understand, explain, and improve our world and the conditions of people.

Lund University welcomes applicants from diverse backgrounds and experiences. Gender equality, equal opportunities, and diversity are fundamental principles for all parts of our operations.

Workplace Description

The position will be located at the Department of Water Resources Engineering, Faculty of Engineering.

Research and teaching are conducted here to understand how water exists and is transported in nature and urban systems from an engineering perspective. This includes, but is not limited to, understanding and modeling the components of the hydrological cycle as well as geospatial modeling of water quality and quantity in agricultural systems. The department has about 35 employees and approximately 20 PhD students. The current position is within the research group Remote Sensing and AI in Hydrology.

The Department of Water Resources Engineering has a stimulating and international environment consisting of PhD students, postdocs, and faculty from many parts of the world. Research and teaching take place in an open and progressive climate with challenges and collaborations both within academia and with industry partners, both nationally and internationally. The work environment is characterized by commitment, collaboration, creativity, and personal responsibility.

Subject Description

The research group Remote Sensing and AI in Hydrology within the Division of Water Resources Engineering focuses on integrating various remote sensing techniques (RS) to measure, for example, land surface deformation, precipitation, etc., including both satellite-based and ground-based radar sensors. This PhD project will investigate the interactions between precipitation (e.g., weather radar data), surface water management (e.g., dam operations, river diversion, and irrigation methods), and groundwater depletion and how these interactions contribute to land subsidence. Using InSAR data, the study will map subsidence patterns in catchment areas globally, particularly in regions where surface water management methods significantly alter groundwater formation. Artificial intelligence (AI) models will be developed to analyze the relationship between surface water management policies, groundwater levels, and subsidence rates. The goal is to propose integrated water management strategies that minimize the risks of subsidence while ensuring sustainable water use. The outcome may include a global map of subsidence risks related to surface water management and RS-AI-based tools for monitoring and predicting subsidence and groundwater levels under various water management scenarios.

Job Responsibilities

The main task of a PhD student is to engage in their doctoral education, which includes participation in research projects as well as doctoral courses. The tasks may also include teaching and other institutional responsibilities, but a maximum of 20% of the working time.

Job responsibilities include:

• organizing seminars
• activities related to PhD students such as organizing social events, field excursions, and departmental PhD students

Eligibility

Basic eligibility for doctoral education is granted to those who have

• completed a degree at the advanced level or
• completed course requirements of at least 240 higher education credits, of which at least 60 higher education credits at the advanced level or
• acquired substantially equivalent knowledge in some other way, within or outside the country.

The requirements for specific eligibility for the doctoral subject are met by those who have:

• At least 120 higher education credits in subjects relevant to the research area, including at least 75 higher education credits at the advanced level in a field relevant to the subject area, or
• A degree at the advanced level in civil and environmental engineering, water and wastewater engineering, or similar.

Other requirements:

• Excellent oral and written skills in English.
• Practical knowledge of remote sensing data analysis (RS), satellite image processing (radar data processing), Google Engine, or artificial intelligence in water-related subjects.
• Programming skills in MATLAB, Python, or similar.

For the full advertisement, please see www.lu.se (www.lu.se)

LTH – Lund University Faculty of Engineering – is the technical faculty at Lund University. At LTH, we educate people, build knowledge for the future, and work hard to develop society. We create space for brilliant research and inspire creative development of technology, architecture, and design. Nearly 10,000 students study here. Each year, our researchers – many of whom operate in world-leading profile areas – publish about 100 theses and 2,000 scientific findings. A number of research results and student projects are refined into innovations. Together we explore and create – for the benefit of the world.

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