Project Overview
The research focuses on probabilistic machine learning for audio applications. The goal is to develop effective representations that generalise across cultures, languages, and styles, and to create methods for sequence modeling, tokenisation, uncertainty quantification, and information retrieval applied to audio analysis tasks.
Responsibilities
* Develop representations for audio datasets that generalise across diverse cultural and linguistic contexts.
* Design, implement, and evaluate sequence modeling, tokenisation, uncertainty quantification, and information retrieval techniques for audio.
* Perform research that bridges cutting‑edge probabilistic ML theory and real‑world applications.
* Collaborate frequently with internal and external researchers to conduct and disseminate research findings.
Qualifications
* Master's degree in Electrical Engineering, Computer Science, or Artificial Intelligence, with solid foundations in probability theory and machine learning.
* Preferable background in speech or audio signal processing.
* Excellent understanding of mathematical concepts in probabilistic machine learning.
* Strong programming skills and ability to prototype complex ML systems.
* Fluent in English with strong written and oral communication skills.
Benefits
* Full‑time PhD scholarship for 1 year, extendible up to 4 years.
* Working in a stimulating environment at Europe’s most innovative university within a well‑equipped, internationally oriented research unit.
* Research located at the Department of Electrical Engineering at the Arenberg Campus in Heverlee (close to Leuven centre).
Equal Opportunity Statement
KU Leuven is committed to creating an inclusive, respectful, and socially safe community. We embrace diversity among individuals and groups as an added value. Open dialogue and differing perspectives are essential in an ambitious research and teaching environment. We recognize the consequences of historical inequalities and do not accept any form of discrimination on the basis of, among others, gender, gender identity and expression, sexual orientation, age, ethnic or national origin, skin colour, religious belief, neurodiversity, work disability, health, or socio‑economic status. For questions regarding accessibility or support offered, please contact our e‑mail address.
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