Open Audio ML Research (Master Thesis / Internship)

About us

Why we need you

The topics we write ourselves come from problems we already know we have. The more interesting ones tend to come from somewhere else: a paper we have not read, a technique borrowed from another field, a question that only looks obvious to someone who has been living inside it. We would rather hear one of those than have you pick from our list. What we can offer in return is the part that is hard to get from inside a university, namely recordings of real attacks, a detection engine running on live traffic, and constraints sharp enough to turn a loose research question into a precise one: a verdict has to land in under 50 ms, and a false alarm costs about as much as a miss. If your idea fits in there, the topic is yours.

What you will do

• Send us a proposal: the question, why it matters, roughly how you would attack it, and what would count as an answer. One or two pages is plenty
• Shape it with us: we will push hard on scope, feasibility, and how it connects to what we run in production, then settle on a version we both want to see finished
• Run the research: experiments, ablations, and honest evaluation, with weekly supervision and a lot of freedom in between
• Test it against real conditions: our data, our latency and false-alarm budget, and audio that has been through real devices, rooms, and phone lines
• Land it: a thesis or report, and where the result holds up, code that goes into our stack

About you

• Pursuing or recently finished an MSc (or equivalent) in CS, EE, ML, or a related field
• A concrete idea you want to work on, and enough grounding in audio, speech, or signal processing to explain why it is worth doing
• Strong Python and hands-on ML, ideally PyTorch
• Willing to argue for your idea and then let it change: we will challenge the proposal, and the best version of it is rarely the first one
• Rigorous about measurement, because a result you cannot evaluate is not a result
• Fluent in English, available for around 6 months, and able to be in the Zurich office at least two days a week

Nice to have

• Published, preprinted, or open-sourced work in audio, speech, or ML
• A university supervisor already lined up, if this is a Master thesis
• Experience with generative audio, deepfake detection, speaker verification, or real-time inference
• A track record of finishing self-directed projects without someone setting the milestones

Benefits

• Paid Master thesis or internship
• Your own topic, chosen by you and taken seriously rather than handed down
• Access to real attack data and a detector running in production, which is hard to come by as a student
• Weekly supervision from people who work on this full-time, without being told what to do day to day
• Flexible hybrid setup: work where you are most productive, with at least two days a week in the Zurich office