Research Specialist

The Applied Mathematics group within the Computational Science Initiative at Brookhaven National Laboratory has an immediate opening for a Research Specialist to work in an interdisciplinary team to address computational challenges in different scientific domains. The successful candidate is expected to design, develop, and integrate novel computational techniques and algorithms, including software and numerical methods for a broad range of scientific applications, including accelerator physics, biology/biomedicine, climate, materials science, and quantum computing. The group's research areas include, but are not limited to: (1) Bayesian uncertainty quantification; (2) optimization; (3) decision making under uncertainty; (4) optimal experimental design; (5) model reduction; (6) scientific machine learning; (7) numerical methods (for PDEs, linear algebra, etc.); (8) modeling & simulation. Essential Duties and Responsibilities: Work in a collaborative team with applied mathematicians, computational scientists, applied mathematicians, computer scientists, and scientific domain scientists Perform innovative research in applied mathematics and scientific computing pertinent to application needs Develop new research ideas and seek out opportunities for funding Present results at meetings, workshops, and conferences Publish findings at conference proceedings and/or academic journals Lead or participate in research grant proposal writing Mentor postdocs and students as needed Required Knowledge, Skills, and Abilities: PhD in Applied Mathematics, Physics, Engineering, Statistics, Operations Research, Computer Science, or a related field Established track records of performing high-caliber research in computational applied mathematics domains, including one or more of: uncertainty quantification, optimization, optimal experimental design, model reduction, numerical methods, or scientific machine learning Authored peer-reviewed publications, and presented at scientific conferences Demonstrated programming skills in Julia, Python, R, C or other commonly used programming languages Experienced in the application of applied mathematics to domain problems in science and engineering The ability to perform independent innovative research under minimal supervision, including the design, development, and validation of research methodologies The ability to work and communicate well within an interdisciplinary team Excellent oral and written communication skills Preferred Knowledge, Skills, and Abilities: 2+ years of post-PhD research experience Familiarity with parallel, distributed, and/or GPU algorithms on high performance computing (HPC) platforms Familiarity with high-level libraries or domain specific languages for probabilistic programming, optimization, differential equations, machine learning, or other numerical methods (e.g., Turing/Stan/Pyro/PyMC3, JuMP/Pyomo, Flux/PyTorch/JAX, etc.) Experienced in applications to accelerator physics, biology/biomedicine, climate, materials science, or quantum computing Knowledge of the application of machine learning methodologies in scientific domains Knowledge of dynamical systems Experience of working in an interdisciplinary team with demonstrated achievements Track record of securing research funding, either as a (Co-)Principal Investigator or key personnel Brookhaven Laboratory is committed to providing fair, equitable and competitive compensation. This is a multi-level role and the full salary range for this position is $93800 - $168000 / year. You will be placed at the level and salary commensurate with your experience. Salary offers will be commensurate with the final candidate's qualification, education and experience and considered with the internal peer group.