The Health Natural Language Processing Lab at Boston Children’s Hospital is seeking a post-doctoral research fellow to contribute to cutting edge research in the field of health natural language processing. This project will develop deep neural network methods for representing and summarizing the text in electronic health records, with high impact clinical applications.

The diversity of subject matter will require a creative candidate with the passion and diligence to solve challenging problems in an interdisciplinary environment. The Research Fellow will be expected to lead publications, and will receive enthusiastic mentorship with the goal of preparing and submitting a career development award proposal, as well as other research proposals as appropriate.

This position provides an excellent opportunity for the Research Fellow to work within a multidisciplinary research team to explore advanced areas in health information technology. CHIP is home to 20 faculty working at the forefront of research areas extending beyond clinical NLP to digital epidemiology, clinical genomics, and app ecosystems for health records. CHIP and the Health NLP Lab value diversity and believe that it is essential to achieving excellence. We therefore strongly encourage candidates from underrepresented groups to apply. The fellowship includes an academic appointment at Harvard Medical School, as well as a hospital appointment at Boston Children’s Hospital.

Admissions

The position is available immediately and is renewable annually.

Qualifications
  • PhD degree in computer science, information science, computational linguistics, biomedical informatics, data mining,
    or a closely related field.
  • Experience in research; ability to plan and carry out research experiments and projects.
  • Candidates with experience in the areas of machine learning, natural language processing/computational linguistics, and medical terminologies/ontologies are strongly encouraged to apply.
  • Programming experience in computer programming languages (e.g., Python, Java, etc).
  • Strong written and oral communication skills required.
  • Ability to work both independently and as a team player. 
How to apply

Interested candidates should email a CV, three letters of reference, and a sample publication to
Prof. Timothy Miller, PI Natural Language Processing Lab tim.miller@gmail.com

Publications

Morales A, Ing A, Antolik C, Austin-Tse C, Baudhuin LM, Bronicki L, Cirino A, Hawley MH, Fietz M, Garcia J, Ho C, Ingles J, Jarinova O, Johnston T, Kelly MA, Kurtz CL, Lebo M, Macaya D, Mahanta L, Maleszewski J, Manrai AK, Murray M, Richard G, Semsarian C, Thomson KL, Winder T, Ware JS, Hershberger RE, Funke BH, Vatta M, . Harmonizing the Collection of Clinical Data on Genetic Testing Requisition Forms to Enhance Variant Interpretation in Hypertrophic Cardiomyopathy (HCM): A Study from the ClinGen Cardiomyopathy Variant Curation Expert Panel. The Journal of molecular diagnostics : JMD 2021.

Kohane IS, Aronow BJ, Avillach P, Beaulieu-Jones BK, Bellazzi R, Bradford RL, Brat GA, Cannataro M, Cimino JJ, García-Barrio N, Gehlenborg N, Ghassemi M, Gutiérrez-Sacristán A, Hanauer DA, Holmes JH, Hong C, Klann JG, Loh NHW, Luo Y, Mandl KD, Mohamad D, Moore JH, Murphy SN, Neuraz A, Ngiam KY, Omenn GS, Palmer N, Patel LP, Pedrera-Jiménez M, Sliz P, South AM, Tan ALM, Taylor DM, Taylor BW, Torti C, Vallejos AK, Wagholikar KB, Weber GM, Cai T. What Every Reader Should Know About Studies Using Electronic Health Record Data but May be Afraid to Ask. Journal of medical Internet research 2021.

Cutillo CM, Sharma KR, Foschini L, Kundu S, Mackintosh M, Mandl KD, . Machine intelligence in healthcare-perspectives on trustworthiness, explainability, usability, and transparency. NPJ digital medicine 2020.

Brat GA, Weber GM, Gehlenborg N, Avillach P, Palmer NP, Chiovato L, Cimino J, Waitman LR, Omenn GS, Malovini A, Moore JH, Beaulieu-Jones BK, Tibollo V, Murphy SN, Yi SL, Keller MS, Bellazzi R, Hanauer DA, Serret-Larmande A, Gutierrez-Sacristan A, Holmes JJ, Bell DS, Mandl KD, Follett RW, Klann JG, Murad DA, Scudeller L, Bucalo M, Kirchoff K, Craig J, Obeid J, Jouhet V, Griffier R, Cossin S, Moal B, Patel LP, Bellasi A, Prokosch HU, Kraska D, Sliz P, Tan ALM, Ngiam KY, Zambelli A, Mowery DL, Schiver E, Devkota B, Bradford RL, Daniar M, Daniel C, Benoit V, Bey R, Paris N, Serre P, Orlova N, Dubiel J, Hilka M, Jannot AS, Breant S, Leblanc J, Griffon N, Burgun A, Bernaux M, Sandrin A, Salamanca E, Cormont S, Ganslandt T, Gradinger T, Champ J, Boeker M, Martel P, Esteve L, Gramfort A, Grisel O, Leprovost D, Moreau T, Varoquaux G, Vie JJ, Wassermann D, Mensch A, Caucheteux C, Haverkamp C, Lemaitre G, Bosari S, Krantz ID, South A, Cai T, Kohane IS. International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium. NPJ digital medicine 2020.