Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
-
Field
-
laser arrays Optimization and acceleration of existing simulation codes Parallel computing and high-performance computing (HPC) GPU acceleration, where appropriate Development of scalable numerical
-
scientific imaging. The primary responsibility of this position is designing and implementing sparse algorithms for large-scale scientific and numerical computations. The successful candidate will pursue
-
of atmospheric dynamics, process scale models, and numerical computation techniques Knowledge of data analysis Knowledge of using atmospheric and hydrological observational datasets, data assimilation techniques
-
of experience within the field. Scientific and numerical programming in Python or R and knowledge of class ML methods and deep learning methods. Well versed in a variety of bioinformatic analyses, including Next
-
modeling with an understanding of numerical methods and physical processes relevant to ocean circulation and dynamics. Experience with high-performance computing, parallel numerical models, large scientific
-
the application deadline. Have a solid background in quantum computing and quantum algorithms, sufficient to conduct research on the topic. Have programming skills (e.g., Python) and experience with numerical
-
field. Candidates should have expertise in several of the following areas: Scientific software development (C++, Julia, Rust, Python, or similar) Numerical methods and scientific computing. High
-
responsible for developing and analysing numerical models that describe progressive damage in laminated composites, with a clear emphasis on transferring detailed fracture mechanisms to models that can be used
-
institution is its access to high-performance computing infrastructure, which is essential for large-scale numerical simulations. Researchers on the OPERA team regularly participate in collaborative projects
-
, or related areas. Strong programming and numerical modelling skills using Python, MATLAB/Simulink, Julia, hardware in the loop implementation or comparable tools. Experience with one or more relevant methods