Johns Hopkins University
Postdoctoral Fellow in Computational Neurolinguistics - #Faculty
Johns Hopkins University, Baltimore, Maryland, United States, 21276
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 (https://www.sciencedirect.com/science/article/pii/S1053811918300429?via%3Dihub) ; 2020 (https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000883) ; 2022 (https://elifesciences.org/articles/72056) ; cf. Lalor et al., 2006 (https://www.sciencedirect.com/science/article/pii/S1053811906006434) ; 2009 (https://journals.physiology.org/doi/full/10.1152/jn.90896.2008?rfr_dat=cr_pub++0pubmed&url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org)
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 (https://onlinelibrary.wiley.com/doi/10.1111/ejn.13748) and e.g., Chalas et al. 2024 (https://www.sciencedirect.com/science/article/pii/S096098222400856X), Meyer et al. 2015 (https://www.sciencedirect.com/science/article/pii/S0010945215002397)
An interest in probing interpretable, theory‑grounded cognitive processes involved in language comprehension, such as memory retrieval and disambiguation
Salary: $62,232 a year
Applicants should submit a CV, research statement, and the contact information of three recommenders to the application in Interfolio.
Job Type: Full Time
The listed salary range represents the minimum and maximum Johns Hopkins University offers for this position, based on a good faith estimate at the time of posting. Actual compensation will vary depending on factors such as location, skills, experience, market conditions, education, and internal equity. Not all candidates will qualify for the highest salary in the range.
Johns Hopkins provides a comprehensive benefits package supporting health, career, and retirement. Learn more: https://hr.jhu.edu/benefits-worklife/.
Equal Opportunity Employer
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
EEO is the Law
https://www.eeoc.gov/sites/default/files/2023-06/22-088EEOCKnowYourRights6.12ScreenRdr.pdf
#J-18808-Ljbffr
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 (https://www.sciencedirect.com/science/article/pii/S1053811918300429?via%3Dihub) ; 2020 (https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000883) ; 2022 (https://elifesciences.org/articles/72056) ; cf. Lalor et al., 2006 (https://www.sciencedirect.com/science/article/pii/S1053811906006434) ; 2009 (https://journals.physiology.org/doi/full/10.1152/jn.90896.2008?rfr_dat=cr_pub++0pubmed&url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org)
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 (https://onlinelibrary.wiley.com/doi/10.1111/ejn.13748) and e.g., Chalas et al. 2024 (https://www.sciencedirect.com/science/article/pii/S096098222400856X), Meyer et al. 2015 (https://www.sciencedirect.com/science/article/pii/S0010945215002397)
An interest in probing interpretable, theory‑grounded cognitive processes involved in language comprehension, such as memory retrieval and disambiguation
Salary: $62,232 a year
Applicants should submit a CV, research statement, and the contact information of three recommenders to the application in Interfolio.
Job Type: Full Time
The listed salary range represents the minimum and maximum Johns Hopkins University offers for this position, based on a good faith estimate at the time of posting. Actual compensation will vary depending on factors such as location, skills, experience, market conditions, education, and internal equity. Not all candidates will qualify for the highest salary in the range.
Johns Hopkins provides a comprehensive benefits package supporting health, career, and retirement. Learn more: https://hr.jhu.edu/benefits-worklife/.
Equal Opportunity Employer
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
EEO is the Law
https://www.eeoc.gov/sites/default/files/2023-06/22-088EEOCKnowYourRights6.12ScreenRdr.pdf
#J-18808-Ljbffr