"Postdoctoral Fellow in Computational Neurolinguistics"

General Description

Salary: $62,232 a year

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.

Qualifications

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 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.