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

Zhang A, Teng L, Alterovitz G. An explainable machine learning platform for pyrazinamide resistance prediction and genetic feature identification of Mycobacterium tuberculosis. Journal of the American Medical Informatics Association : JAMIA 2020.

Geva A, Stedman JP, Manzi SF, Lin C, Savova GK, Avillach P, Mandl KD. Adverse drug event presentation and tracking (ADEPT): semiautomated, high throughput pharmacovigilance using real-world data. JAMIA open 2020.

Börcsök J, Sztupinszki Z, Bekele R, Gao SP, Diossy M, Samant AS, Dillon KM, Tisza V, Spisák S, Rusz O, Csabai I, Pappot H, Frazier ZJ, Konieczkowski DJ, Liu D, Vasani N, Rodrigues JA, Solit DB, Hoffman-Censits JH, Plimack ER, Rosenberg JE, Lazaro JB, Taplin ME, Iyer G, Brunak S, Lozsa R, Van Allen EM, Szüts D, Mouw KW, Szallasi Z. Identification of a synthetic lethal relationship between nucleotide excision repair (NER) deficiency and irofulven sensitivity in urothelial cancer. Clinical cancer research : an official journal of the American Association for Cancer Research 2020.

Gokuldass A, Draghi A, Papp K, Borch TH, Nielsen M, Westergaard MCW, Andersen R, Schina A, Bol KF, Chamberlain CA, Presti M, Met Ö, Harbst K, Lauss M, Soraggi S, Csabai I, Szállási Z, Jönsson G, Svane IM, Donia M. Qualitative Analysis of Tumor-Infiltrating Lymphocytes across Human Tumor Types Reveals a Higher Proportion of Bystander CD8 T Cells in Non-Melanoma Cancers Compared to Melanoma. Cancers 2020.

Perera G, Rijnbeek PR, Alexander M, Ansell D, Avillach P, Duarte-Salles T, Gordon MF, Lapi F, Mayer MA, Pasqua A, Pedersen L, van Der Lei J, Visser PJ, Stewart R. Vascular and metabolic risk factor differences prior to dementia diagnosis: a multidatabase case-control study using European electronic health records. BMJ open 2020.