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
Fellowship, 2020 application is open.
Due January 15 2020
https://www.krellinst.org/csgf/
Established in 1991, the Department of Energy Computational Science Graduate
Fellowship (DOE CSGF) provides outstanding benefits and opportunities to
students pursuing doctoral degrees in fields that use high-performance
computing 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 impact on
the nation while advancing their research. Fellows come from diverse scientific
and engineering disciplines but share a common interest in using computing in
their research. More than 425 students at more than 60 U.S. universities have
trained as fellows. The program's alumni work in DOE laboratories, 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
interdisciplinary. There is also a Math/Computer Science track is intended for
candidates focusing on fundamental research into enabling technologies for
high-performance computing (HPC) that are broadly relevant to science and
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 are
exposed to the computational needs of applications that will use these new
enabling technologies.
Chris Penland, PhD
Director, Clinical Pharmacology, ADME, and AI (CPAA)
__________________________________________
AstraZeneca
R&D | Clinical Pharmacology & Safety Sciences
35 Gatehouse Dr, Waltham, MA USA 02451
T: +1 781 839 4618 M: +1 617 275 3769
[email protected]<mailto:[email protected]>
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