DATA Call for Student & Postdoctoral Fellow Research Posters

Call for Student & Postdoctoral Fellow Research Posters Presented at the DATA Industry Advisory Board Fall 2024 Meeting September 25, 2024, Michigan League University of Michigan, Ann Arbor 

The Center for Data-Driven Drug Development and Treatment Assessment (DATA), a national, NSF-sponsored Industry-University Cooperative Research Center (IUCRC) is inviting students, postdoctoral fellows and other trainees to submit research project posters to showcase during its Industry Advisory Board (IAB) meeting on September 25, 2024, in Ann Arbor, Michigan. 

We are interested in trainees’ projects that apply computational methods, data science solutions, artificial intelligence (AI) and machine learning (ML) techniques to solve problems in health care, pharmacy, chemistry, and biomedical engineering, in particular in the areas of drug design, drug repositioning, and treatment assessment. 

The meeting will include a number of companies from the pharmaceutical, healthcare, technology, and AI startup industries, as well as representatives of governmental agencies, offering the presenters ample opportunities to network and discuss their career plans with potential employers. In addition to the poster showcase, the meeting agenda includes research panels and roundtables focused on the use of AI in drug design and treatment optimization. 

University of Michigan students of all levels (undergraduate, master’s, doctorate), postdoctoral fellows and other trainees are eligible to participate. 

Interested applicants should submit an abstract of their project(s) by September 16th, 2024, using the following link.

POSTER SUBMISSION FORM

About DATA 

The Center for Data-Driven Drug Development and Treatment Assessment (DATA) advances U.S. competitiveness by working with industry to solve current, emerging, and industry-relevant challenges in drug design, drug repositioning and repurposing, treatment monitoring, assessment and optimization, patient phenotyping, and quantitative pharmacovigilance using novel computational and data science techniques such as metrology, machine learning, and/or artificial intelligence (AI), including generative AI, and by training the next generation of talent in this field. DATA seeks to produce new methodologies and infrastructure for industry-wide collaborative drug discovery and treatment assessment, with the goal of significantly accelerating the pace of drug development to help target the right drug to the right person at the right safe and effective dose while reducing R&D costs.