| The Research Associate position involves design, analysis and implementation of algorithms for solving problems in areas of large-scale scientific computing combined with state-of-the-art deep learning data science methodology. The specific fields include but are not limited to:
(i) Scalable Solvers for (Very) High-Order Finite Element Problems
(ii) Multilevel Methods for Training Encoder-Decoder Recurrent Neural Networks for Large-Scale Data Sets.
The work is a collaboration with teams of researchers at Lawrence Livermore National Laboratory.
It is expected that the suitable candidate will pursue independent but complementary research, contribute to project progress reports, give presentations at conferences and publish in peer reviewed specialized numerical analysis/scientific computing/data science journals.
Minimum qualifications:
PhD or another appropriate combination of educational achievement and professional expertise.
Knowledge of finite elements (f.e.) theory, some knowledge of deep learning algorithms, experience with publicly available scalable f.e. libraries, as well as proficiency in parallel computing and expertise in the programming languages C++ and PyTorch are required.
Inquiries may be directed to Professor Panayot Vassilevski, grant PI, panayot@pdx.edu. |
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