Skip to main navigation Skip to search Skip to main content

Beginnings: Introducing Molecular Modeling Experiences to Underrepresented Students

Project: Research

Abstract & Details

Description

Award ID: 2322496

Molecular modeling plays a pivotal role in biotechnology and drug discovery. To foster innovation in this field, it is crucial for biotechnology companies to have a diverse and well-trained workforce. One way to expand access to this field is by equipping undergraduate students from underrepresented backgrounds with industry-level knowledge. Experiential learning opportunities, in which students learn-by-doing, is a particularly effective way to broaden participation of these groups because it provides technical skills needed by industry. A partnership between local public academia and industry will promote the progress of science by providing better training to future scientists and advance national health by broadening the pool of well-qualified biotechnology workforce members. This project aims to empower underrepresented minority students to actively participate in the biotechnology field and bring their unique perspectives to the table. By diversifying the STEM workforce, the goals are to enhance innovation, foster creativity, and develop more inclusive solutions to global problems. The initiative Introducing Molecular Modeling Experiences to underRepresented StudEnts (IMMERSE) aims to promote a cross-sector partnership between LaGuardia Community College and the New York City College of Technology, both of the City University of New York (CUNY) system; and Schrdinger, a biotechnology industry leader in molecular modeling software, headquartered in New York City. The goal is to prepare undergraduate students for molecular modeling careers by incorporating industry-driven problems to the classroom, and subsequent participation in a Schrdinger internship program, that then leads to specialized careers. To achieve this goal, the project team will provide experiential learning in a multistage approach. First, in the classroom, students will be taught molecular modeling concepts relevant to biotechnology. Second, hands-on training will be provided to illustrate how to use the molecular modeling tools in a step-by-step format. Third, paid internship opportunities at Schrdinger will be provided, where students will learn-by-doing, while also working on industry-relevant problems that will make them more competitive in the job market. The expected outcome is highly trained and diverse students who are well-equipped for a career in computational biotechnology. This project aligns with the NSF ExLENT Program, funded by the NSF TIP and EDU Directorates, as it seeks to support experiential learning opportunities for individuals from diverse professional and educational backgrounds to increase their interest in, and their access to, career pathways in emerging technology fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Karen Crosby
StatusClosed
Effective start/end date10/01/23 → 04/25/25

Funding

  • (ExLENT) Experiential Learning for Emerging and Novel Technologies : $998,665.00

Active Fiscal Year

  • FY2024
  • FY2025

Start Fiscal Year

  • FY2024

TIP Programs

  • (ExLENT) Experiential Learning for Emerging and Novel Technologies

Key Technology Areas

  • Biotechnology
  • (confidence score: 100%)

Technology Foci

  • Biotechnology - Other than SynBio
  • (confidence score: 91%)
  • Genomics and bioinformatics
  • (confidence score: 100%)

Congressional District at Award

  • District n. 07 of New York

Current Congressional District

  • District n. 07 of New York

United States

  • New York

Core Based Statistical Area (CBSA)

  • New York-Newark-Jersey City, NY-NJ

County

  • County: Kings, NY

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint. Learn more about Elsevier's Fingerprint Engine here: https://beta.elsevier.com/products/elsevier-fingerprint-engine