St. John’s, established in 1870, has three New York City campuses; international locations in Rome, Italy; Limerick, Ireland; and Paris, France; and study abroad locations around the world. The Princeton Review and other top rankings consistently recognize the University’s outstanding academics, diverse student body, dynamic internship, and volunteer opportunities, focus on student life, and diverse study abroad offerings. St. John’s University offers more than 100 undergraduate and graduate programs in its six colleges and schools, with a growing number of programs offered online. The University is accredited by the Middle States Commission on Higher Education and 12 other major academic and professional associations. 

A dedication to diversity, equity and inclusion is at the heart of our mission.  As a Catholic and Vincentian university, St. John’s is committed to institutionalizing practices of inclusive excellence to ensure that we welcome and celebrate the intrinsic worth of all members of our community.  We will become an even stronger university as we enhance equity at every level of our institution. Our graduates will excel in the competencies and values needed for leadership and service in a rapidly evolving world.  

Position:  Researcher Assistant (1-year position, with the possibility of extension based on performance)

Location: Bukhari Lab, St. John's University, New York

Supervisor: Dr. Syed Ahmad Chan Bukhari, Associate Professor

Position Overview:

We are seeking a highly motivated and skilled Post-bacc /Postgrad Researcher to join our research team focused on developing a trustworthy medical code recommender system. This NSF-supported project addresses critical challenges in medical coding and reimbursement, such as erroneous manual coding, limitations of current computer-assisted coding (CAC) systems, and the trustworthiness of emerging technologies like large language models (LLMs). The successful candidate will play a key role in advancing AI methodologies to enhance the accuracy, transparency, and trustworthiness of medical coding systems.

Why Join Us?

• Be part of cutting-edge research that seeks to revolutionize medical coding and improve healthcare reimbursement processes.
• Work in a dynamic, interdisciplinary environment with access to state-of-the-art technologies and resources.
• Opportunity to develop innovative solutions that directly impact the healthcare industry and patient outcomes.
• Potential for career growth with a position that can be extended based on performance.

Key Responsibilities:

• Research and develop and implement a novel AI framework leveraging deep learning, NLP, LLMs, and knowledge graphs to address hallucination and accuracy issues in automated coding.
• Build and integrate knowledge graphs and structured data representations to improve system decision-making.
• Collaborate on developing a socio-technical feedback loop that incorporates input from healthcare professionals, refining system recommendations dynamically.
• Engage with a multidisciplinary team, including AI researchers, medical informaticists, and healthcare professionals, to validate and improve the system.

Qualifications:

• BS or master’s in computer science, Biomedical Informatics, Machine Learning, Artificial Intelligence, or a related field. Candidates with master's degrees and experience developing medical software are encouraged to apply.
• Strong programming and software development skills, particularly in Python, with experience in AI/ML libraries such as TensorFlow, PyTorch, or similar.
• Expertise in deep learning, NLP, LLMs, knowledge graph construction, API development, and web-based applications.
• Familiarity with healthcare standardization systems such as HL7, ICD-10, and SNOMED CT.

Application Process:

Interested candidates should submit:

1. A cover letter detailing your research interests, relevant skills, and motivation for applying.
2. A current CV, including a list of publications.
3. A link to your GitHub profile or a list of development projects specifying your role and contributions.
4. Document containing contact information for three professional references.

Applications will be reviewed on a rolling basis until the position is filled.

Position Type/Expected Hours of Work:

This is a part-time, temporary 1-year position, with the possibility of extension based on performance. Hours may vary. Occasional long hours may be required based on project demands

In compliance with NYC’s Pay Transparency Act, the hourly salary range for this position is $20 - $25 per hour. St. John’s University considers factors such as (but not limited to) scope and responsibilities of the position, candidate’s work experience, education/training, key skills, internal peer equity, as well as market and organizational considerations when extending an offer.  

This position may be relocated to any of St. John’s University’s physical locations at any time.

Any offer of employment is subject to receipt by St. John’s University of satisfactory references, verification of employment and education. 

St. John’s University is an Equal Opportunity Employer and encourages applications from women and minorities. All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national or ethnic origin, age, status as an individual with a disability, protected veteran status, or any other characteristic protected by law.