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DOE Computational Science Graduate Fellowships Applications Open

From: Penland, Chris <Chris.Penland>
Date: Tue, 29 Oct 2019 00:58:02 +0000

Fellow pharmacometricians -
I am an alumnus of this fellowship and am willing to discuss my experiences=
, and its history with any interested advisors or students.
Please reach out.
Chris Penland PhD

Announcing the US Department of Energy Computational Sciences Graduate Fell=
owship, 2020 application is open.
Due January 15 2020

Established in 1991, the Department of Energy Computational Science Graduat=
e Fellowship (DOE CSGF) provides outstanding benefits and opportunities to =
students pursuing doctoral degrees in fields that use high-performance comp=
uting to solve complex science and engineering problems.

The program fosters a community of energetic and committed Ph.D. students, =
alumni, DOE laboratory staff and other scientists who want to have an impac=
t on the nation while advancing their research. Fellows come from diverse s=
cientific and engineering disciplines but share a common interest in using =
computing in their research. More than 425 students at more than 60 U.S. un=
iversities have trained as fellows. The program's alumni work in DOE labora=
tories, private industry and educational institutions.

Each successful candidate for the traditional DOE CSGF must have a specific=
 science or engineering application for their research. This is typically i=
nterdisciplinary. There is also a Math/Computer Science track is intended f=
or candidates focusing on fundamental research into enabling technologies f=
or high-performance computing (HPC) that are broadly relevant to science an=
d engineering applications of interest to DOE.
Such areas include (but are not limited to):

  * ODE, PDE, and integral discretization methods
  * Linear and nonlinear solvers
  * Multiscale, multi-physics coupling methods
  * Verification, validation, and uncertainty quantification
  * In situ data analysis
  * High-dimensional data analysis
  * Large-scale data visualization
  * High-performance compilers
  * Programming models and abstractions for heterogeneous computing
  * Domain-specific languages
  * Dynamic runtime environments
  * Power management
  * HPC development tools
  * HPC performance analysis and tools
  * Debugging at extreme scale
  * Scalable I/O
  * Scalable machine learning
  * Interpretable machine learning
  * Physics-constrained machine learning
  * Robust machine learning
  * Scientific data management and engineering
The interdisciplinary program of study for fellows in this track will still=
 include science and engineering course requirements, ensuring that they ar=
e exposed to the computational needs of applications that will use these ne=
w enabling technologies.

Chris Penland, PhD
Director, Clinical Pharmacology, ADME, and AI (CPAA)
R&D | Clinical Pharmacology & Safety Sciences
35 Gatehouse Dr, Waltham, MA USA 02451
T: +1 781 839 4618 M: +1 617 275 3769

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Received on Mon Oct 28 2019 - 20:58:02 EDT

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