PhD Position — Aarhus University
4 days ago
Data-driven analysis of turbulence and algae dynamics using high-frequency lake monitoring data
Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Ecoscience programme. The position is available from 01 September 2025 or later.
Research area and project description:
Lake water management needs reliable projections by aquatic ecosystem models; however, these models underperform for non-linear and complex processes, i.e., deep-water mixing as well as algae dynamics. Technological advancements in sensor technology and data collection have significantly increased the volumes of data available for limnologists. At the same time, deep learning algorithms have revolutionized the sciences by their ability to extract information from data of complex systems. This sets up our conundrum: how can we, in a sound way, infer information from environmental data to update our scientific understanding and improve our model projections?
To tackle this challenge, this project aims to apply deep learning algorithms on high-frequency monitoring data for scientific discovery of, e.g., governing equations and driving factors. For this, the prospective PhD student will conduct field work and apply methods for data-driven discovery. Specifically, the candidate will:
(1) Support the deployment of additional high-frequency loggers at Lake Ravn, Denmark, and conduct regular microstructure profiler measurements of turbulent mixing and fluorescence at the lake. The data are essential to capture vertical turbulent fluxes, and the growth and decay of algae blooms inside the water column.
(2) Curate high-frequency data from global sources to increase data volumes. This will be done through international networks (e.g., Global Lake Ecological Observatory Network) and bottom-up data calls. Data will be quality-controlled and standardized to create a database of time-dynamic, depth-discrete water quality measurements.
(3) Apply exploratory machine learning techniques (e.g., Shapley values for sensitivity analysis, physics-informed neural networks for solving inverse problems, and sparse identification algorithms) on the high-frequency data set to discover underlying relationships. These projections will be compared to state-of-the-art vertical 1D aquatic ecosystem models to discuss their uncertainty.
This PhD project is part of "Integrating AI into Aquatic Ecosystem Models to Decode Ecological Complexity", funded by Villum Fonden, that aims to develop innovative mathematical models to improve our projections and understanding of non-linear aquatic ecosystem processes. The successful outcome of this project has the potential to pave the way for a new generation of cutting-edge technologies for ecological research, and to improve our conceptual understanding of two critical ecosystem processes: turbulent transport on the physical side, and algae dynamics on the ecological side. Improving our understanding of both will support future management efforts to mitigate the formation of harmful algae blooms. The selected PhD student will gain skills and knowledge in limnological field monitoring, data science, environmental fluid dynamics, phytoplankton ecology, and deep learning. The project will involve close work with colleagues at the Department of Ecoscience as well as active collaborations with scientific institutions in North America and Europe with the opportunity to undergo research stays.
In the Computational Limnology team at the Freshwater Ecology section at Aarhus University, we are focused on having a diverse, fair, and inclusive team and work environment. Team members support each other and help out when we see someone physically or mentally struggling. We work respectfully with people from different backgrounds, experiences, and nationalities. To collaborate more efficiently and ensure reproducibility, we implement the principles of open data and open science.
Qualifications and specific competences:
Applicants must have a relevant Master's degree (120 ECTS), or equivalent, or receive the Master's degree before the start date. The Master's degree should be in Environmental Science, Environmental Engineering, Ecology, Applied Mathematics, Data Science, or a related discipline.
Experience in scientific programming with, e.g., Python and/or R, is required. Basic understanding of freshwater ecology and physical limnology is an advantage. Interest in conducting field work (e.g., setting up buoys and conducting regular samplings), numerical modelling, and applying machine learning and deep learning methods to environmental data is highly desirable.
The ideal candidate should demonstrate motivation and curiosity, possess strong teamwork abilities, exhibit the potential to produce high-impact publications, and have excellent proficiency in English.
Feel free to write directly to Robert Ladwig, rladwig@ecos.au.dk , if you have more questions.
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