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Position Summary
Pseudotime trajectory analysis is widely used to infer differentiation paths such as disease progression. However, traditional methods designed for scRNA-Seq data do not account for the spatial location of cells since the data is generated from dissociated tissues. This lack of contextual information may cause the method to overlook region-specific changes, especially when the same cell type is distributed across different regions. Now there is an opportunity to address this gap with the advent of spatial transcriptomics (ST) technologies, which generate whole transcriptome profiles linked to physical location and tissue morphology. By incorporating spatial information, we expect to reconstruct more accurate and biologically faithful trajectories, enabling the identification of potential targets that drive the progression of disease states.
In this internship, we aim to review published spatial trajectory analysis methods, compare one or two methods with a baseline model, and test it on well-characterized ST datasets. This project offers a unique opportunity to contribute to innovative target identification approaches while working alongside a dynamic and collaborative team at BMS. Interns will gain hands-on experience working with large-scale ST data and cutting-edge methods at the interface between machine learning and computational biology. They will also acquire biological insights into complex diseases by learning from bench biologist colleagues. Additionally, interns will have the chance to connect with colleagues across different teams, providing a broader understanding of drug development and hopefully helping to shape their future career plans.
Key Responsibilities
Explore and evaluate a published spatial trajectory analysis method.
Apply the selected method to a well-characterized ST dataset and interpret disease state changes, with support from other colleagues.
If time allows, benchmark the method against baseline models and other published methods.
Qualifications & Experience
PhD student in Computational Biology, Computer Science, Bioinformatics, Biostatistics, or related fields.
Experience using Python to analyze genomics data.
Experience working in high-performance computing (HPC) and Linux environments.
Experience with imaging data analysis is preferred but not required.
Knowledge of R would be a plus but not required.
If you come across a role that intrigues you but doesn't perfectly line up with your resume, we encourage you to apply anyway. You could be one step away from work that will transform your life and career.
Uniquely Interesting Work, Life-changing Careers
With a single vision as inspiring as "Transforming patients' lives through science™ ", every BMS employee plays an integral role in work that goes far beyond ordinary. Each of us is empowered to apply our individual talents and unique perspectives in an inclusive culture, promoting diversity in clinical trials, while our shared values of passion, innovation, urgency, accountability, inclusion and integrity bring out the highest potential of each of our colleagues.
On-site Protocol
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Site-essential roles require 100% of shifts onsite at your assigned facility. Site-by-design roles may be eligible for a hybrid work model with at least 50% onsite at your assigned facility. For these roles, onsite presence is considered an essential job function and is critical to collaboration, innovation, productivity, and a positive Company culture. For field-based and remote-by-design roles the ability to physically travel to visit customers, patients or business partners and to attend meetings on behalf of BMS as directed is an essential job function.
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