Early-Stage Researcher position: Observing Whitecapping-Driven Wave Dissipation in Coastal Ocean with Synthetic Aperture Radar
Wave breaking controls air–sea momentum and gas exchange, upper-ocean turbulence, and wave-energy dissipation, but dissipation is poorly constrained in coastal seas. This PhD will develop and test methods to estimate whitecapping dissipation from SAR, wave spectra, and other in situ data, to build robust dissipation proxies, assess their validity in the Baltic Sea, and identify when and where SAR constrains breaking-related energy loss.
The project will address the following key areas:
The project will combine Sentinel-1 and other C-/X-band SAR data with wave-buoy spectra, meteorological forcing, and where available ADCP or echosounder observations. The methodological emphasis is on empirical, machine-learning, and physics-informed approaches for retrieving breaking-sensitive metrics from SAR and linking them to wave dissipation estimates derived from collocated sea-state information.
The study includes defining robust predictors of whitecapping intensity, testing sensitivity to wind sea versus swell, incidence angle, viewing geometry, and environmental confounders, and quantifying uncertainties in dissipation-oriented retrievals. The broader aim is to improve process-level understanding and provide new observational constraints relevant to coastal wave modelling and air–sea interaction studies.
The thesis should address the following questions:
- Which SAR observables and processing workflows are most informative for whitecapping intensity and wave-energy dissipation in coastal seas?
- How well can dissipation proxies be derived from collocated wave spectra, sea-state descriptors, and SAR imagery across different forcing regimes?
- How strongly do confounding factors such as viewing geometry, rain, sea-state maturity, or wind–wave misalignment affect dissipation retrieval skill?
- What are the spatial and temporal limits within which dissipation-related SAR retrievals remain robust in the Baltic Sea?
Responsibilities and (foreseen) tasks:
- Assemble and quality-control multi-sensor datasets from SAR missions, wave buoys, meteorological forcing products, and relevant in situ observations.
- Develop and validate empirical, machine-learning, and physics-informed methods to retrieve whitecapping-sensitive metrics and dissipation-related proxies from SAR and collocated sea-state data.
- Quantify the sensitivity of dissipation estimates to frequency range, averaging choices, and environmental confounders.
- Analyse the spatial and temporal variability of whitecapping-driven dissipation in the Baltic Sea, including uncertainty assessment and comparison with model data.
- Support teaching activities of Dr. Sander Rikka and contribute to the broader research activities of the Department of Marine Systems.
Applicants should fulfil the following requirements:
- A master’s degree in Natural Sciences or Engineering, preferably in oceanography, physics, geophysics, remote sensing, data science, applied mathematics, or a closely related field.
- A clear interest in the topic of the position.
- Good programming skills in at least one relevant language, preferably Python. Experience with MATLAB or R is also useful.
- Excellent communication of English.
- Strong and demonstrable writing and analytical skills.
- Capacity to work both as an independent researcher and as part of an international team.
The candidate should submit a research plan for the topic, including the overall research and data collection strategy. The candidate can expand on the listed research questions and tasks, and propose theoretical lenses to be used.
The following experience is beneficial:
- Knowledge of Linux/Unix systems, reproducible scientific workflows, and High-Performance Computing (HPC) environments.
- Previous experience with satellite or in situ geophysical datasets, databases or data services (e.g. Copernicus), and statistical or machine-learning methods.
- Previous experience in analysing geospatial or environmental data, such as remote-sensing imagery, wave or atmospheric model fields, or acoustic observations.
- It is desirable if the candidate can share some of their GitHub projects to demonstrate programming skills.
We offer:
- 4-year full time (fully funded) PhD position in an outstanding Baltic Sea research institution with a large portfolio of ongoing pan-European and national public sector applied research projects
- Opportunity to participate in applied research projects funded by European Commission (e.g. LIFE program), European Space Agency (ESA)
- Opportunities for conference visits, research stays and networking with globally leading universities and research centers in the fields of applications of machine learning, satellite data processing and oceanography
Supervisors:
Main supervisor: Senior Researcher Sander Rikka, School of Science: Department of Marine Systems, Division of Modeling and Remote Sensing
Co-Supervisor: Tenured Associate Professor Sven Nõmm, School of Information Technologies, Department of Software Science: Machine Learning Research Group
Tallinn University of Technology (TalTech), Estonia’s only technological university, is the country’s flagship for engineering and natural sciences. The Department of Marine Systems is a leading oceanographic and meteorological R&D unit in the Baltic Sea region. We study oceanographic processes to uncover cause‑and‑effect relationships and develop marine monitoring and forecasting services. Our methods include machine‑learning‑based satellite image and model data analysis, as well as innovative operational techniques for monitoring the marine environment and its changes. With long‑term experience in operational oceanography, we provide information products on water levels, ice conditions, and other marine physical parameters to the public and authorities. We contribute significantly to the pan‑European Copernicus Marine Service (CMEMS) and the Destination Earth (DestinE) initiative.
For information about the admission process, please visit the PhD Admission homepage or contact us at [email protected]
Applications can be submitted from 14.08.2026 to 13.09.2026
