IBM Social Good Fellowship - overview
WE ARE CURRENTLY SOLICITING APPLICANTS FOR OUR 2017 PROGRAM!
We will begin reaching out to semi-finalists by approximately February 1, 2017. Final decisions are expected by approximately February 15, 2017.
Mentoring, giving back, making a difference…
We are experiencing a time when our lives and everything that surrounds us is captured digitally: Internet activity, video, customer transactions, surveys, health records, news, literature, scientific publications, economic data, weather data, geospatial data, stock market returns, telecommunication records, and government records to name a few. All of this data is at our fingertips, giving us an unprecedented opportunity to change the world for the better using data science. From reducing or eliminating inequalities, to improving access to health care and education, to reducing pollution and our carbon footprint, the opportunities are endless.
The IBM Social Good Fellowship is an opportunity for undergraduate and graduate students as well as postdoctoral scholars to develop their skills and develop data science solutions that benefit humanity. Mentored by leading IBM Research scientists and engineers at the T. J. Watson Research Center in Yorktown Heights, NY (north of New York City), fellows use data mining, machine learning, analytics, operations research, statistics, and mobile computing methods to complete projects with social impact.Working closely with non-governmental organizations, social enterprises, government agencies, and other mission-driven partners, fellows take on real-world problems in health, energy, environment, education, international development, equality, justice, and more.This fellowship has been created in collaboration with Corporate Citizenship as part of their mission to bring the best of IBM's capabilities to solving societal problems.
If you are an NGO, or a social enterprise, we are currently scoping projects for our 2017 cycle. If you have an idea how we can help, drop us an email, and we will follow up.