A $1 million, 3-year grant from the Howard Hughes Medical Institute (HHMI) awarded jointly to three prominent research universities all located in Newark, NJ will be used to develop a novel doctoral program designed to train future neuroscientists who can integrate approaches used in mathematics, biomedical sciences and computation as they investigate emerging questions in the neural sciences. The consortium of the New Jersey Institute of Technology (NJIT), Rutgers-Newark, and the University of Medicine and Dentistry of New Jersey (UMDNJ)-New Jersey Medical School was among 10 awardees selected from 132 applicants.
The inter-institutional quantitative neurosciences doctoral training program will be co-directed by Joshua Berlin of UMDNJ-New Jersey Medical School, Robert Miura of NJIT and James Tepper of Rutgers-Newark. It will employ state-of-the-art interdisciplinary approaches to explore and better explain the complex mechanisms underlying nerve and brain function.
"Although tremendous progress has been made in the neurosciences, daunting challenges remain," Tepper said. "The solutions to these problems are likely to be found by bringing together the tools and approaches from many different disciplines. The HHMI award will allow us to train a new generation of scientists who can work at the interface of quantitative, computational and biological science to address these problems."
Miura said that by working together, the three universities will be able to create a particularly effective doctoral program. "The HHMI training grant represents a significant new opportunity to take advantage of the myriad complementary strengths of the faculty of UMDNJ-New Jersey Medical School, Rutgers-Newark and NJIT," he noted. "With their physical proximity and close ties among the faculty, these three institutions will create a unique environment unparalleled in interdisciplinary neuroscience training."
Berlin agreed. "The HHMI programPage: 1 2 3 Related biology news :1
Contact: Sheryl Weinstein
New Jersey Institute of Technology
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