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]> Please consider the environment before printing this e-mail ________________________________ Confidentiality Notice: This message is private and may contain confidential and proprietary information. If you have received this message in error, please notify us and remove it from your system and note that you must not copy, distribute or take any action in reliance on it. Any unauthorized use or disclosure of the contents of this message is not permitted and may be unlawful.
