Qualifications:

Independence – be comfortable executing activities and needs

Good written and verbal communication

Outstanding teamwork

Experience on algorithm development, numerical methods, and scientific computing

Strong background in solid and fracture mechanics

Experience with computational materials science methodologies

Familiar with machine learning and deep learning concepts and applications

Familiar with FEA methods and implementations

Experience with Bash, Python, Perl/TCl/MATLAB, or C++

Experience with modeling software Previous software development and Linux experience

Strong debug and problem-solving skills

Experience writing reports and/or conference papers

Experience with regression, neural networks, and frameworks

Ability to work in a team environment and experience with remote collaboration tools Background in semiconductor process development or integration is encouraged

Education Requirements:

Pursuing Masters or Ph.D. in Chemical Engineering, Applied Chemistry, Applied Physics, Applied Mathematics, Statistics, Materials Science, Electrical Engineering, or Computer Science within the past two years