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Americas, United States, Berkeley

Posted 25 days ago

Computational Career-Track Research Scientist

🏛 Berkeley Lab

USD 94K - 227K per year

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Role Type

🧪 Scientist / Researcher

Role Focus

🛠️ Advance Science

Seniority

🌿 Experienced
🌸 PhD / Postdoc

Employer Type

🏛️ Government

This role is for a Computational Career-Track Research Scientist focused on research and development in high-performance computing (HPC). The scientist will concentrate on algorithms related to sparse linear and tensor algebra, randomized algorithms, and quantum algorithms. A key goal is applying these advanced algorithms to model and simulate physical systems, integrating uncertainty quantification and AI/ML techniques.

Key Responsibilities

Contribute to designing and integrating advanced algorithms for sparse structured linear and eigen systems.

Develop algorithms related to linear algebra and tensors specifically for quantum computing.

Contribute to advancing Bayesian statistical methods for robust and scalable uncertainty quantification (UQ).

Assist with code optimization and integration into Department of Energy (DOE) applications utilizing exascale computer systems with GPU accelerators.

Required Skills

PhD or equivalent experience in applied mathematics, computer science, or a related field.

Demonstrated experience in developing algebraic solvers.

Proficiency in more than one computer programming language, such as Python, C/C++, CUDA, or Fortran.

Knowledge of complexity and performance analysis and advanced data structures.

Experience using MPI, OpenMP, and other parallel programming models.

Understanding of quantum many-body problem and tensor eigenvalue problem.

Excellent oral and written communication skills.

Nice-to-have Skills

Postdoctoral experience in a relevant field.

Experience in programming massively parallel computer platforms.

Technology Tags

Linear algebra

The role focuses heavily on sparse linear systems and linear algebra algorithms.

Tensor Networks

The research involves sparse linear and tensor algebra, especially related to quantum computing problems.

Many-Body Systems

Required knowledge includes understanding of the quantum many-body problem.

High-Performance Computing

A core responsibility is R&D activities in high-performance computing, optimization, and integration.

ML approaches

The role involves applying AI/ML techniques for scientific disciplines and developing novel AI/ML methodology.

Hybrid algorithms

The research involves integrating classical HPC techniques, AI/ML, and quantum algorithms for solving complex problems.

Variational Quantum Eigensolver

The focus on linear/tensor eigenvalue problems and the quantum many-body problem strongly suggests VQE or related variational methods.

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