Postdoctoral Position in AI, Machine Learning and Auditory Neuroscience

We are recruiting a postdoctoral researcher to focused on exploration and development of AI models of auditory perception, towards a broader goal of understanding how the brain predicts and learns from human communication sounds such as speech and music. The project sits at the intersection of machine learning, neuroscience, and behavior, with applications ranging from music cognition to speech-in-noise perception in audiology. You will work with naturalistic audio, large datasets, and multimodal neural recordings (EEG, fMRI, intracranial) to develop models with a major goal of interpretability so that we may better understand the influence of complex, natural communication signals on our mental processes., * Quantify prediction, uncertainty, and learning in real-world stimuli and track how they are encoded in neural data

• Develop tools to be used in audiological clinical to diagnose patients based on naturalistic interactions.
• Work with an interdisciplinary team to better understand neural function in healthy and patient populations.
• Communicate your work with non-experts in ML domains to facilitate collaboration
• Publish peer-reviewed articles based on your findings.

You will have flexibility to shape your research direction within the broader theme of predictive processing.

Why join this project?

This position offers something different from standard ML roles. Rather than focusing only on performance, we aim to understand how models work and what they reveal about the brain.

You will:

• Own your project and drive ideas from concept to publication
• Work on problems with scientific depth and long-term impact
• Develop models that influence both neuroscience and AI research

This is an ideal position if you want to:

• build an independent academic profile, or
• strengthen your expertise for roles in industry or startups

The combination of technical depth, interdisciplinary research, and intellectual ownership provides a strong platform for future career development in both academia and tech.

• PhD in ML, computational neuroscience, physics, engineering, or related field
• Strong experience in machine learning (PyTorch or equivalent)
• Experience with sequence or time-series models, * Audio, speech, or music processing
• Transformers, reinforcement learning, or probabilistic models
• Interest in brain and behavior