Assistant Computational Scientist @ Brookhaven National Laboratory
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Job Details
Overview
Brookhaven National Laboratory is seeking an Assistant Computational Scientist to join the CDS Computational Science Department. This role focuses on designing, developing and integrating computational techniques for scientific applications.
Key Responsibilities
- Collaborate with interdisciplinary teams of scientists, mathematicians and engineers.
- Conduct innovative research in high performance computing, machine learning and quantum computing.
- Develop and implement novel software and numerical methods.
- Present research findings at meetings, workshops and conferences.
- Contribute to research grant proposals and mentor junior staff.
Position Requirements
Candidates should have a strong background in computational science with proven research experience. Required qualifications include a relevant degree with corresponding years of work or research experience, demonstrated programming skills in C/C++, Python, Fortran or similar languages, and experience in high performance computing environments.
Preferred Qualifications
Additional experience with GPU accelerators (CUDA, HIP, SYCL, etc.), distributed parallel computing (MPI, NCCL), complex workflow management, and applying artificial intelligence in scientific domains is desired.
Other Information
This is an on-site position located in Upton, NY. Moderate domestic and international travel is expected. Detailed benefits information and site access requirements (REAL-ID compliant documentation) apply.
Key skills/competency
Computational Science, High Performance Computing, Machine Learning, Numerical Methods, Software Development, C/C++, Python, Research, Simulation, Interdisciplinary
How to Get Hired at Brookhaven National Laboratory
🎯 Tips for Getting Hired
- Customize your resume: Highlight computational research and programming experience.
- Align with requirements: Emphasize HPC and machine learning skills.
- Showcase projects: Include relevant simulation and numerical methods work.
- Prepare for interviews: Review research contributions and technical challenges.