Master’s thesis; Multi-Modal Machine Listening for Bioacoustic Analysis and Modelling

Introduction: The AukLab-Audio Initiative
The AukLab-Audio project represents a cutting-edge, long-term data collection effort at the Stora Karlsö Auk Lab, a unique cliff-edge observatory for common guillemots (Uria aalge) in the Baltic Sea. This initiative is generating an unprecedented multi-channel, multi-modal dataset, featuring continuous, synchronized audio recordings from an array of high-quality DPA microphones and camera-integrated microphones, alongside extensive video footage, weather data, and other sensor information. The richness of this dataset opens up unparalleled opportunities for research at the intersection of bioacoustics, machine learning, and marine ecology. We are excited to offer four interconnected Master’s thesis projects designed to explore different facets of this unique dataset. These projects aim to foster a collaborative research environment, where students will contribute distinct expertise towards a unified goal: to deepen our understanding of guillemot vocal behavior, develop novel computational analysis techniques for complex acoustic scenes, and derive new ecological insights. A potential research field trip to the Stora Karlsö research site may be organized to provide students with first-hand experience of the data collection environment and the study species. We invite applications from motivated students for the following project:

Master’s thesis project
• Focus: This project centers on developing and applying machine listening models that leverage the multi-modal nature of the AukLab-Audio dataset. The student will explore how existing signals, such as visually detected events (e.g., prey deliveries, agonistic interactions from video analysis) or environmental conditions (e.g., weather data), can be used to train, supervise, or contextualize acoustic models.

• Potential Tasks:

• Investigating multi-modal fusion techniques for sound event detection and classification (e.g., combining audio features with visual cues or weather patterns).
• Developing weakly supervised or self-supervised learning approaches that utilize cooccurring visual or sensor data to learn acoustic representations.
• Exploring the correlation between specific vocalizations and environmental/behavioral contexts identified from other data streams.
• Building predictive models for guillemot behavior or colony state based on integrated acoustic and non-acoustic data.

• Main Supervisor: Olof Mogren

Collaborative Environment and Unified Goals
Several master’s thesis projects will run at the same time and are designed to be highly synergistic. Regular joint meetings and knowledge sharing will be encouraged to ensure that developments in one project can inform and accelerate progress in others. The ultimate aim is to collectively advance our understanding of common guillemot ecology through innovative computational analysis of the rich AukLab-Audio dataset, contributing to both ecological science and machine listening methodology.

What We Offer

• Supervision by an interdisciplinary team of researchers with expertise in machine learning, bioacoustics, and marine ecology.
• Access to the unique, large-scale AukLab-Audio dataset and computational resources.
• An opportunity to contribute to cutting-edge research with potential for high-impact publications.
• A collaborative and stimulating research environment.
• Potential for a research field trip to the Stora Karlsö field station.
• After a successful project completion, RISE will pay each student SEK 30 000.

Who are you?
We are looking for two motivated students with a strong background in machine learning, signal processing — particularly audio — and programming in Python, using libraries such as PyTorch, TensorFlow, or Librosa. An interest in bioacoustics, animal behavior, or applied AI for environmental applications is highly valued. Experience with acoustic data and large-scale machine learning frameworks will be considered a strong advantage.

Terms

• Location: Gothenburg
• Time: January to June, 2026
• Suitable for a team of two students

Welcome with your application!
Interested candidates should submit their application at the latest November 30, with:

• A curriculum vitae (CV).
• Academic transcripts. 

Be brief in your application. List relevant meriting projects and experience (only).

Contact: Olof Mogren, olof.mogren@ri.se.

We encourage students that are interested in research to apply, and will provide the support needed to write and submit a paper to a relevant conference at the end of the project if the student wants to do so. This can be very beneficial if you want to apply for a PhD position in the future. We look forward to receiving your applications and exploring the fascinating world of guillemot communication together!

Related thesis projects at RISE and SLU

• Project 1: Multi-Modal Machine Listening for Bioacoustic Analysis and Modelling

• Project 2: Data-Centric AI for Acoustic Discovery: Anomaly Detection and Active Learning in Bioacoustics

• Project 3: Ecological Insights from Acoustic Monitoring: Annotation, Behavioral Linkage, and Environmental Drivers of Guillemot Vocalizations

• Project 4: Bio-Shazam: Audio Fingerprinting for Bioacoustics analysis

Read more about these projects at https://www.ri.se/en/about-rise/work-with-us/ open-job-positions.