Current Deadlines & Submission Form

• Nominations open: March 15, 2024

• Nominations close: April 30 , 2024

• Notifications by: July 30, 2024

Nominators can begin the process at the how to nominate page.

Award Information

ACM SIGHPC has created the Computational and Data Science Fellowships, a continuation of the program started with Intel (see below) to increase the diversity of students pursuing graduate degrees in data science and computational science. Specifically targeted at women or students from racial/ethnic backgrounds that have not traditionally participated in the computing field, the program is open to students pursuing degrees at institutions anywhere in the world.

For the purposes of these fellowships, “computational science” encompasses any program of study where computational modeling and simulation serve as the primary methods for conducting research, typically in a field other than computer science (e.g., computational chemistry, wildfire modeling, computational hydrodynamics).  Similarly, “data science” relies on computational analysis of large-scale data as the basis for research (e.g., ecological informatics, financial analytics). Preference will be given to candidates whose background is from disciplines other than computer science and who can bring new perspectives to computational/data science.

Applications for the fellowships involve three independent components and must be submitted using SIGHPC’s online nomination system:

1. Nomination:   submitted by the student’s advisor (or soon-to-be advisor), who will explain how the candidate qualifies for a fellowship

2. CV and candidate statement:  submitted by the student, along with contact information for an endorser

3. Brief endorsement:  submitted by a current or former instructor, project supervisor, or employer who has personal knowledge of the student’s past accomplishments and can speak to the candidate’s suitability

All components must be submitted in sequence, and completed by the submission deadline. See how to nominate for details. In accordance with ACM policies on conflict-of-interest, the following are ineligible to serve as nominators or endorsers:  officers of ACM and members of the Fellowship Selection Committee.

Fellowship recipients will be evaluated based on their overall potential for excellence in data science and/or computational science, their likelihood of successfully completing a graduate degree, and the extent to which they will serve to increase diversity in the workplace.

Each fellowship recipient will receive a stipend prior to the start of their first academic term after August 1. The value of the stipend will be US$15,000 annually, adjusted depending on the country where the degree will be earned (using  the most recent national price level ratio published by the World Bank). This stipend is intended to augment, not replace, the support already being provided by the institution. PhD recipients will receive the stipend annually for up to 4 years and MS recipients up to 2 years, as long as they are deemed to be making appropriate progress in the degree program (progress will be evaluated annually by ACM SIGHPC based on a brief report from each recipient). If additional funding becomes available, fellowship winners may be given the opportunity to receive extended support (through the completion of the degree, but not more than four years total).

For individuals with a permanent residence outside of the United States that receive a monetary award from SIGHPC, ACM will withhold 30% of the award value for US Taxes in compliance with Internal Revenue Services. Based on the individual's location they may be able to reclaim the tax withholding amount. ACM and SIGHPC will not provide assistance in reclaiming the withheld taxes. 

New fellowship recipients will be recognized formally at the annual SC conference’s Awards Session. SIGHPC and SC will provide travel support for this (including airfare, hotel, conference registration, and an expense stipend).

Questions?  See how to nominate and the FAQs for more information.