The thesis work focuses on the hydraulic characterization of a new fish swimming flume, which is crucial for the restoration of river connectivity and effective fish passage solutions. The goal is to quantify the hydraulic characteristics of the facility using experimental flow measurement techniques such as Acoustic Doppler Velocimetry (ADV) and Particle Image Velocimetry (PIV).

Responsibilities:

• Investigate the relationship between pump RPM, number of operating pumps, discharge, and mean flow velocity.
• Characterize the development of the flow field within the swimming flume using PIV.
• Evaluate the flow field within the outlet pool using ADV.
• Validate an existing CFD model against the collected measurement data.

Qualifications:

• Currently pursuing studies in Environmental Engineering, Hydraulic Engineering, Civil Engineering, or a related field.
• Strong communication skills in English, both written and spoken.
• Basic understanding of hydraulics and fluid mechanics.
• Interest in conducting hands-on experimental work in a research environment.
• Strong interest in ecohydraulics and aquatic ecosystems.
• Ability to work independently and take initiative in a research project.

Additional Information:

• The scope of the thesis work is 30 ECTS credits (approximately 6 months).
• The start date is February/March 2027 or by agreement.
• The location is Älvkarleby, Sweden, with a hybrid solution possible.

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Are you a student looking to apply your knowledge to real-world solutions that support the energy transition? With us, you will have the opportunity to complete your thesis project close to the business, working alongside experienced specialists and tackling challenges where your ideas and perspectives can make a real difference.

About the Thesis Work

The goal of this thesis is to develop a data fusion approach that combines satellite imagery, drone imagery/photogrammetry and ROV sonar measurements to produce accurate, high-resolution bathymetric maps for a shallow water area that is subjected to water regulation for hydropower generation by Vattenfall.

The project will investigate how machine learning / deep learning methods can be used to align, calibrate and merge these heterogeneous, multi-resolution data sources into a single consistent depth model, using the sparse but accurate ROV measurements to correct and validate the wide-area satellite- and drone-derived depth estimates.

A further purpose of the thesis is to combine the fused bathymetry with above-water elevation and terrain data to build a full 3D reconstruction of the basin, which will be used to estimate the water volume of the area.

The following steps are foreseen to execute the project:

• Literature review of satellite-derived bathymetry, drone-based photogrammetry/LiDAR and ROV sonar surveying methods, and existing data fusion techniques.
• Data acquisition and collection of satellite, drone and ROV data for the area of interest.
• Pre-process and co-register the multi-source data (satellite, drone, ROV) to a common spatial and depth reference.
• Develop and apply a data fusion model (e.g. machine learning / deep learning based) to combine the multi-source bathymetric data into a unified depth map.
• Perform elevation and terrain analysis and build a 3D reconstruction of the basin (bathymetry + surrounding terrain) to estimate the water volume.
• Evaluate and validate the fused bathymetry model performance against reference/ground-truth measurements.

Several field campaigns will be carried out during the thesis period to collect validation data.

Qualifications

We are looking for a student who are about to complete their academic studies and are eager to apply their knowledge in a real-world setting through a thesis project at Vattenfall. You are someone who shares our values and identifies with our principles: Active, Open, Positive and Safety.

• Enrolled in an MSc programme in Geomatics, Remote Sensing, Computer Science, Applied Physics, Environmental or Civil Engineering, or a related field.
• Strong written and verbal communication skills in English. Swedish language skills are considered an advantage.
• Strong programming skills in Python.
• Experience with data analysis and relevant tools such as NumPy, PyTorch, TensorFlow, GDAL, Rasterio, or GIS software.
• Knowledge of, or a strong interest in, remote sensing, image processing, machine learning, and deep learning.
• Basic understanding of GIS principles and geospatial data formats, including raster data, point clouds, and coordinate systems.
• Interest in applying satellite-based data to environmental analysis and biodiversity-related challenges.
• Experience working with satellite imagery, drone data, or sonar/acoustic survey data is considered an advantage.
• Ability to work independently, structure and document work systematically, and communicate results effectively.

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