Job Description

Posting Summary
The An-Cockrell Lab at the University of Vermont seeks a subject matter expert in applied machine learning to lead the technical development of AI/ML models for the lab’s ARPA-H-funded research portfolio. Working independently with general direction from the Principal Investigator, this position designs, builds, and deploys machine learning models in a secure Amazon Web Services (AWS) environment using modern tools such as SageMaker. The position also sets the standards and practices other lab staff follow in that environment.

The role oversees the continued development of models that use electronic health record (EHR) and medical sensor data to predict the need for life-saving interventions. It also serves as a technical lead on the CIRCLE project, a multi-year effort to build a digital twin around the Innate Immune Response Agent-Based Model (IIRABM). This work includes designing multi-organ compartment and pharmacokinetic/pharmacodynamic (PK/PD) extensions, and applying active learning methods, including Genetic Algorithms and Deep Reinforcement Learning, to calibrate the model and develop curated drug regimens that support healing. The position also applies large language model (LLM) fine-tuning and prompt engineering to medical applications.

This person solves complex, undefined research problems where established methods may not exist, and their work has a direct impact on project milestones and deliverables. The position trains, guides, and reviews the work of junior technical staff. It serves as a primary technical contact with ARPA-H program officers and external collaborators, and contributes technical content, budgets, and computing plans to future grant proposals.
Minimum Qualifications (or equivalent combination of education and experience)
1) A MS or higher in engineering, computer science, physics or mathematics plus 3-5 years of applied ML experience.
2) Proficient with Python
3) Knowledge and experience using tools to integre machine learning algorithms in secure cloud environments (e.g., Docker).
4) Proficient with AWS
5) Proficient with SageMaker
4) Knowledge and experience with machine learning and artificial intelligence techniques.
5) Demonstrated track record of working well with a multi-disciplinary team of researchers.
6) Works independently with general direction from the principal investigator as needed.
7) History of successful academic productivity in terms of publications or presentations at national-level meetings.
8) Ability to gain a thorough knowledge and understanding of university policies, procedures, and guidelines.