Summer 2025 - Bioanalysis Artificial Intelligence and Machine Learning Internship

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• Position Summary
• Artificial intelligence (AI) and machine learning (ML) are transforming pharmaceutical research & development (R&D) by shortening drug development times and increasing efficiency.
AI/ML techniques are currently being used to enhance multiple areas of R&D, including drug candidate selection, molecular compound design, and identification of new biomarkers.
These techniques can also have a positive impact on bioanalysis of chiral drugs by accelerating and improving method development.
Chiral separation is a critical component of bioanalytical study support, as two enantiomers of a chiral compound often exhibit different pharmacokinetic (PK)/pharmacodynamic (PD) properties, leading to different therapeutic/toxicological effects.
Traditional chiral method development using liquid chromatography (LC), or supercritical fluid chromatography (SFC) can be time-consuming and resource intensive.
This project aims to examine the potential use of AI/ML to predict enantioresolution (Rs) of CelMod/LDD drug candidates on chiral selectors (or columns) to minimize extensive chiral column screening.
This internship will provide hands-on experience in computer programming, machine learning and data analysis, as well as exposure to chromatographic separation and its applications in bioanalysis, offering a unique opportunity to contribute to cutting-edge research in the field.
• Key Responsibilities
• The student will be responsible for:

Creating a dataset:
Compile a dataset of CelMod/LDD chiral compounds with known chromatographic conditions and enantioresolution on a specific chiral column (chiral selector).
Calculating descriptors:
Calculate relevant molecular descriptors, including those that capture the stereochemical properties of the chiral compounds.
Initial Model Development:
Develop a preliminary ML model to correlate molecular descriptors with experimental enantioresolution (Rs).
Model Optimization:
Use experimental data to train the ML model to assess its predictability for chiral separation (Rs).
By analyzing experimental enantioresolution data along with multiple chiral recognition mechanisms, this ML model could provide valuable insights for chiral separation and recommendations on chiral columns for starting LC/SFC assays, needed for future chiral drug candidates.
This innovative approach has the potential to significantly accelerate the development of chiral separation methods, reduce experimental costs, and increase the throughput in the bioanalysis of development compounds.
• Qualifications & Experience
• The ideal candidate for this internship should have a computer science background, preferably at M.S.
or PhD student level, with experience in model development, coding and data analysis using Python, Java, or other computer programing languages.
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
• BMS has a diverse occupancy structure that determines where an employee is required to conduct their work.
This structure includes site-essential, site-by-design, field-based and remote-by-design jobs.
The occupancy type that you are assigned is determined by the nature and responsibilities of your role:

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.
BMS is dedicated to ensuring that people with disabilities can excel through a transparent recruitment process, reasonable workplace accommodations/adjustments and ongoing support in their roles.
Applicants can request a reasonable workplace accommodation/adjustment prior to accepting a job offer.
If you require reasonable accommodations/adjustments in completing this application, or in any part of the recruitment process, direct your inquiries to *** .
Visit careers.bms.com/ (***/eeo-accessibility) eeo-accessibility to access our complete Equal Employment Opportunity statement.
BMS cares about your well-being and the well-being of our staff, customers, patients, and communities.
As a result, the Company strongly recommends that all employees be fully vaccinated for Covid-19 and keep up to date with Covid-19 boosters.
BMS will consider for employment qualified applicants with arrest and conviction records, pursuant to applicable laws in your area.
If you live in or expect to work from Los Angeles County if hired for this position, please visit this page for important additional information:
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• Company:
• Bristol-Myers Squibb
• Req Number:
• R1585586
• Updated:
• 2024-10-31 03:
33:
57.300 UTC
• Location:
• Princeton-NJ
Bristol Myers Squibb is an equal opportunity employer.
Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status, pregnancy, citizenship, marital status, gender expression, genetic information, political affiliation, or any other characteristic protected by law.