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

Keloth VK, Banda JM, Gurley M, Heider PM, Kennedy G, Liu H, Liu F, Miller T, Natarajan K, V Patterson O, Peng Y, Raja K, Reeves RM, Rouhizadeh M, Shi J, Wang X, Wang Y, Wei WQ, Williams AE, Zhang R, Belenkaya R, Reich C, Blacketer C, Ryan P, Hripcsak G, Elhadad N, Xu H. Representing and Utilizing Clinical Textual Data for Real World Studies: An OHDSI Approach. Journal of biomedical informatics 2023.

Toce MS, Michelson KA, Hudgins JD, Olson KL, Monuteaux MC, Bourgeois FT. Association of prescription drug monitoring programs with benzodiazepine prescription dispensation and overdose in adolescents and young adults. Clinical toxicology (Philadelphia, Pa.) 2023.

Brown T, de Salazar Munoz PM, Bhatia A, Bunda B, Williams EK, Bor D, Miller JS, Mohareb A, Thierauf J, Yang W, Villalba J, Naranbai V, Garcia Beltran W, Miller TE, Kress D, Stelljes K, Johnson K, Larremore D, Lennerz J, Iafrate AJ, Balsari S, Buckee C, Grad Y. Geographically skewed recruitment and COVID-19 seroprevalence estimates: a cross-sectional serosurveillance study and mathematical modelling analysis. BMJ open 2023.

El-Hayek C, Barzegar S, Faux N, Doyle K, Pillai P, Mutch SJ, Vaisey A, Ward R, Sanci L, Dunn AG, Hellard ME, Hocking JS, Verspoor K, Boyle DI. An evaluation of existing text de-identification tools for use with patient progress notes from Australian general practice. International journal of medical informatics 2023.

Patik I, Redhu NS, Eran A, Bao B, Nandy A, Tang Y, El Sayed S, Shen Z, Glickman J, Fox JG, Snapper SB, Horwitz BH. The IL-10 receptor inhibits cell extrinsic signals necessary for STAT1-dependent macrophage accumulation during colitis. Mucosal immunology 2023.
more