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Summer Intern PhD Student - Drug Product Development Engineering

Job in New Brunswick, Middlesex County, New Jersey, 08933, USA
Listing for: Bristol-Myers Squibb Co
Seasonal/Temporary, Apprenticeship/Internship position
Listed on 2026-10-17
Job specializations:
  • Engineering
  • Research/Development
    Data Scientist
Salary/Wage Range or Industry Benchmark: 55000 - 69000 USD Yearly USD 55000.00 69000.00 YEAR
Job Description & How to Apply Below
Position: 2027 Summer Intern PhD Student - Drug Product Development Engineering

At Bristol Myers Squibb, our employees often ask, “Who are you working for?”—a question that fuels collaboration, accountability, and urgency in our work.

Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases.

We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits.

This is work that transforms the lives of patients, and the careers of those who do it.

Drug Product Development works with colleagues throughout the entire pharmaceutical development process, from discovery to commercial launch and beyond, to develop novel formulations, processes, and medical devices that enable drug products at clinical and commercial stages.

We are looking for candidates who are proven team players; appreciate the challenge of pursuing unique scientific discovery in an emerging field; and who demonstrate solid operational, interpersonal, project management and collaborative skills.

We believe this starts with our summer intern program.

Summer interns will work with mentors for ~10 weeks on a guided research project related to applications of predictive modeling, data science, and AI/ML to drug product development.

Depending on experience and interests, interns will design and implement computational models that analyze the effects of process variables and material properties on product attributes; apply machine learning and artificial intelligence techniques to large datasets to drive insights and mitigate product-related risks; develop and test predictive models of key pharmaceutical unit operations for oral solid and parenteral dosage forms, including drug delivery devices;

collaborate with formulation, process development, and device engineering teams; and present results to a diverse audience at internal forums.

The program is organized to ensure that interns gain valuable industry and functional experience, work on challenging assignments, learn about BMS and meet diverse company representatives.

Program will feature:
  • An orientation, including an overview of company structure, products and brands
  • An introduction to the GPS and PD organizations
  • Exposure to senior management and business leaders
  • Hands-on training and working experiences in pharmaceutical manufacturing process areas and testing laboratories
  • Formal presentation at the end of your internship
  • A mentor program
  • Formal review and feedback sessions
Program Dates:

Monday June 7, 2027 - Friday, August 13, 2027.

Applications are currently being accepted and may be selected on a rolling basis. Those selected will be notified no later than February 2027. Position is in New Brunswick, NJ.

Requirements:

Candidates must be currently enrolled in an accredited university program seeking a PhD degree in engineering, materials science, computational sciences, or a related quantitative discipline. Candidate must still be enrolled during Summer 2027. Candidates are expected to graduate between Fall 2027 and 2028. Must include GPA on resume for consideration (program requires GPA of 3.0 or greater), along with a summary of current research, not exceeding two pages.

All candidates must be authorized to work in the US both at the time of hire and for the duration of their employment. Please note that immigration or visa sponsorship is not available for this position. Must have strong oral/written communication skills, with the ability to present technical information to both scientific and non-technical audiences, and be able to work effectively in a collaborative, multidisciplinary team environment.

Ability to solve problems independently with minimal supervision. Demonstrated…

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