2026 Future Talent Program - Translational Genome Analytics, Computational Biology - Co-op

MSD • South San Francisco, CA

2d • Onsite

About The Position

The Future Talent Program features Cooperative (Co-op) education that lasts up to 6 months and will include one or more projects. These opportunities in our Research and Development Division can provide you with great development and a chance to see if we are the right company for your long-term goals The Future Talent Program within the Research and Development Data, AI, and Genome Sciences (DAGS) Department is offering co-op opportunities that last a period of six months within the Translational Genome Analytics group. We have 2 positions at multiple US locations. Candidates can apply for the opportunity to be involved in one or more of the computational biology projects listed below. These projects are designed to enable students to further develop their skills and to evaluate if we are the right company for their long-term goals. We are seeking highly motivated and talented students with computational skills and experience. The projects will be in the following areas: Translational Genome Analytics (1 position, On Site in San Francisco, CA): • Develop interpretable AI/ML models and generate insights to delineate the mechanistic underpinnings of drug effects, but also the distribution effects across a heterogeneous population of tumor models, to enable data-driven inference of rational drug combinations. • Analyze large-scale anti-tumor drug and/or genetic perturbations and sensitivity screens (e.g. DepMap, PRISM, Tahoe-100M) with state-of-the-art machine learning methods with the goal to generate comprehensive associations between background cellular contexts, transcriptional responses, and drug sensitivity/resistance • Mine large-scale internal and external datasets (e.g. RNA-seq, single cell RNA-seq, ATAC-seq, and DRUG-seq) with state-of-the-art methods and enable discovery of therapeutic targets and precision biomarkers in oncology. Translational Genome Analytics (1 position, On Site in Cambridge MA): • Analyze large-scale Alzheimer’s disease multi-omic datasets (e.g. WGS, RNA-seq, single cell RNA-seq, ATAC-seq, etc) with state-of-the-art methods to decode disease-associated molecular alterations • Fine-tune pre-trained transformer models using disease-specific multi-omic datasets to delineate co-regulated gene modules with the goal to distinguish Alzheimer’s disease associated gene expression patterns at the bulk and/or single-cell level • Evaluate the accuracy and utility of cutting-edge pre-trained AL/ML transformer models in imputing disease-related transcriptomic patterns thus enabling the integration of sparse multi-omics disease datasets to drive discovery of therapeutic targets and/or patient stratification biomarkers in Alzheimer’s disease.

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. Students enrolled in graduate programs (MS or PhD) are highly encouraged to apply.
• 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.

Nice To Haves

• 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.
• For these computational projects, 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern