Applied Materials is the leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. Our expertise in modifying materials at atomic levels and on an industrial scale enables customers to transform possibilities into reality. At Applied Materials, our innovations make possible the technology shaping the future.
Our Team
The Computer-Aided Engineering (CAE) team is a group of simulation experts working towards design, development and optimization of Applied Materials chambers and processes. We are currently looking for a Computational Materials Scientist – Intern to join our team.
Your Opportunity
As an intern, you will be primarily involved in developing classical force-fields using simulations and machine learning algorithms. You will be working on challenging problems at the intersection of materials science and machine learning alongside experts in the field.
Roles and Responsibility
Our Team
The Computer-Aided Engineering (CAE) team is a group of simulation experts working towards design, development and optimization of Applied Materials chambers and processes. We are currently looking for a Computational Materials Scientist – Intern to join our team.
Your Opportunity
As an intern, you will be primarily involved in developing classical force-fields using simulations and machine learning algorithms. You will be working on challenging problems at the intersection of materials science and machine learning alongside experts in the field.
Roles and Responsibility
- Model interactions between a set of materials using simulations
- Parameterize force-fields for material systems using Machine Learning algorithms
- Couple simulations to higher length- and time-scale simulations
Our Ideal Candidate
- Currently pursuing PhD in Physics, Materials Science, Chemistry, or related field
- Strong background in density functional theory (DFT) methods and Machine Learning algorithms
- Basic understanding of classical Molecular Dynamics simulations
Qualifications
- Currently pursuing PhD in Physics, Materials Science, Chemistry, or related field
- Strong background in density functional theory (DFT) methods and Machine Learning algorithms
- Basic understanding of classical Molecular Dynamics simulations
Additional Qualifications:
- Basic knowledge of semiconductor processes such as etch (ex: plasma, wet), deposition (ex: atomic layer deposition, chemical vapor deposition, physical vapor deposition), epitaxy, thermal, doping, and other surface modification processes is preferred
- Ability to interact effectively with a broad range of colleagues such as hardware engineers, process engineers, program managers, and computational scientists
Qualifications
Education:
Skills
Certifications:
Languages:
Years of Experience:
Work Experience:
Additional Information
Travel:
Yes, 10% of the TimeRelocation Eligible:
Yes
Applied Materials is committed to diversity in its workforce including Equal Employment Opportunity for Minorities, Females, Protected Veterans and Individuals with Disabilities.
Source link
