PhD Research Scientist – GPU HPC & SPH Tool Development

Universidade de Vigo; España; On-site; EUR 24,000 - 32,000; Full time

Job summary

Universidade de Vigo in Spain seeks a researcher to develop and optimize numerical tools in DualSPHysics, with GPU programming using CUDA, and to improve interaction and collision models within the AURORA project.

The role involves validating numerical models against experimental data, applying data analysis and machine learning techniques, and engaging in an academic environment with opportunities for professional growth.

Qualifications

• Academic training and professional experience at universities, public organizations or companies.
• Experience in scientific programming, high-performance computing (HPC) and GPU programming using CUDA.
• Experience in the development and use of numerical particle methods, particularly SPH and DualSPHysics, and in the modelling of fluid-structure interaction, solid dynamics, and contact and collision models, including Project Chrono or equivalent tools.
• Experience in validation of numerical models against experimental data, data analysis and application of machine learning techniques.
• Personal interview. The interview will be conducted only with those candidates who, in the sum of the merits, reach a minimum of 50 points.

Responsibilities

• Development and optimisation of numerical tools in DualSPHysics, GPU programming using CUDA, improvement of interaction and collision models, validation against experimental data, and support for data analysis and machine learning techniques within the AURORA project.

Skills

Foreign languages

Scientific programming

High-performance computing

GPU programming

Data analysis

Machine learning

Tools

DualSPHysics

CUDA

Project Chrono

Job description

Universidade de Vigo in Spain seeks a researcher to develop and optimize numerical tools in DualSPHysics, with GPU programming using CUDA, and to improve interaction and collision models within the AURORA project.

The role involves validating numerical models against experimental data, applying data analysis and machine learning techniques, and engaging in an academic environment with opportunities for professional growth.