📁Postdoctoral Fellow💼CR-Computational Research86439
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The Data Analytics & Visualization (DAV) Group and the Center for Computational Sciences and Engineering (CCSE) in Berkeley Lab’s Computational Research Division have a joint opening for a Computational Science Postdoctoral Scholar working on data analysis for Exascale Computing Project (ECP) applications.
You will focus on development of in situ data analysis and visualization methods, with a focus on topological data analysis, and coupling of these methods to simulation codes, in particular to application codes built on the block-structured adaptive mesh refinement core infrastructure AMReX. You will join an interdisciplinary team of scientists and engineers in extending the current functionality and performance portability of SENSEI, Ascent, and vtk-m and couple these to AMReX-based and other simulation codes. The incumbent will work with simulation code application teams to identify new data analysis needs and implement appropriate algorithms for performing these analyses in situ.
What You Will Do:
- Research, development, and coding of new and existing algorithms, tools and technologies to meet data-intensive, data-driven science challenges.
- Participate in the extension of Ascent, SENSI and vtk-m core functionality to new analyses and to work effectively on hybrid CPU/GPU systems in the context of hierarchical parallelism on large-scale computing hardware, and assist developers with the coupling this functionality into complex AMReX-based and other application codes.
- Use performance profiling tools to identify impediments to effectively exploiting algorithmic parallelism, and propose and assess improved implementation strategies as appropriate.
- Work independently and collaboratively in a multidisciplinary team environment including mathematicians, computer/computational scientists, and domain scientists (e.g., in biology, ecology, earth sciences, physics, nuclear science, cosmology, energy technologies).
- Author peer-reviewed journal articles and contribute to grant proposals.
What Is Required:
- Ph.D. in Computer Science, Applied Mathematics or the Physical Sciences/Engineering within the last 3 years, with a strong research background in scientific visualization and data analysis, parallel programming, applied mathematics, and scientific computing.
- Strong C++ programming experience.
- Experience with MPI and/or OpenMP.
- Experience in implementing algorithms on shared memory many-core architectures, in particular GPUs. Experience with vtk-m a plus.
- Keen interest in collaborating with scientists to develop new data analysis and visualization methods for new architectures and apply these methods to large-scale simulations.
- Excellent verbal and written communication skills.
- Ability to work productively independently and collaboratively as part of a diverse team.
- Established record of peer reviewed publications.
- Proven experience writing and/or adapting software.
- Keen interest in solving science challenges.
Additional Desired Qualifications:
- Background in scientific visualization and data analysis, ideally with knowledge of topological data analysis.
- Understanding of advanced computer and systems architecture and their contributions to the overall system performance.
- Experience using performance analysis tools.
- Experience with Python.
- Experience with version control systems such as git.
The posting shall remain open until the position is filled.
- This is a full time, 1 year, postdoctoral appointment with the possibility of renewal based upon satisfactory job performance, continuing availability of funds and ongoing operational needs. You must have less than 4 years paid postdoctoral experience. Salary for Postdoctoral positions depends on years of experience post-degree.
- Full-time, M-F, exempt (monthly paid) from overtime pay.
- This position is represented by a union for collective bargaining purposes.
- Salary will be predetermined based on postdoctoral step rates.
- This position may be subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.
- Work will be primarily performed at: Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA.