Postdoctoral Fellow in Computational Neurolinguistics Full-time

Postdoctoral Fellow in Computational Neurolinguistics

The Hale research group in the Department of Cognitive Science at Johns Hopkins University seeks an outstanding postdoctoral fellow to begin July 1, 2026 for a one-year initial, contract-renewable appointment. The primary responsibility of the appointee will be to analyze naturalistic MEG data, with an aim of shedding light on the cross-linguistic mechanisms underlying human sentence processing.

Required Qualifications

• PhD in an area of cognitive science or a closely neighboring field such as cognitive neuroscience, linguistics, computer science, or engineering
• Experience in preprocessing and analyzing SQUID MEG using standard libraries such as MNE-Python or FieldTrip
• Familiarity with magnetoencephalographic source localization techniques
• Ability to apply temporal response function- (TRF-) style analyses to naturalistic MEG data, as in Brodbeck et al. (2018; 2020; 2022; cf. Lalor et al., 2006; 2009)
• Strong programming skills and the ability to write clean, shareable, methodologically-communicable, and well-documented research code
• Fundamental knowledge of machine learning, inferential statistics, and linguistics
• Ability to work as part of a larger research group and coordinate with other team members

Preferred Qualifications

• Academic domain expertise in (computational) psycho- and neurolinguistics
• Conceptual understanding of modern neural language models (Transformers, RNNs) and the ability to interact with them via e.g., the Hugging Face and PyTorch libraries
• An interest in oscillatory and/or connectivity dynamics during naturalistic language comprehension, as in Meyer (2017) and e.g., Chalas et al. (2024), Meyer et al. (2015)
• An interest in probing interpretable, theory-grounded cognitive processes involved in language comprehension, such as memory retrieval and disambiguation

Application Instructions

Applicants should submit a CV, research statement, and the contact information of three recommenders to the application in Interfolio.