About the role

Develop interpretable AI/ML models and generate insights to delineate the mechanistic underpinnings of drug effects, as well as the distribution effects across a heterogeneous population of tumor models.
Analyze large-scale anti-tumor drug and/or genetic perturbations and sensitivity screens with state-of-the-art machine learning methods.
Mine large-scale internal and external datasets to enable discovery of therapeutic targets and precision biomarkers in oncology.
Analyze large-scale Alzheimer’s disease multi-omic datasets with state-of-the-art methods.
Fine-tune pre-trained transformer models using disease-specific multi-omic datasets.
Evaluate the accuracy and utility of cutting-edge pre-trained AI/ML transformer models in imputing disease-related transcriptomic patterns.

Requirements

Candidates must be currently enrolled in a minimum of a Bachelor's degree program in Biomedical Engineering, Computer Science, Biological Sciences or a related field of study.
Candidates must be available to work full-time for 6 months in 2026.
Candidates must have excellent academic achievement and strong data science abilities.
Candidates must have demonstrated superior communication and interpersonal skills and must be able to work well in a collaborative team environment.
Candidates should have interests/experience in ANY of the fields of oncology, immunology or neuroscience, but are expected to have a strong emphasis on data science, AI/ML, computational biology and/or experience with large-scale molecular profiling data analyses.
Specific skill sets should include one or more of the following: experience with R or Python programming languages, basic biostatistics, analysis of bulk or single cell datasets.