Postdoctoral Scholar  - 106045

Berkeley Lab’s Center for Advanced Mathematics for Energy Research Applications (CAMERA) is seeking a Postdoctoral Scholar to develop advanced mathematics and algorithms for analyzing complex data from DOE experimental facilities. The role involves conducting research in areas such as optimization, machine learning, and computer vision, while collaborating with multidisciplinary teams to deliver user-friendly software solutions.

Responsibilities

• Conduct independent and collaborative research to develop new mathematics and algorithms for analyzing complex data from DOE experimental facilities
• Develop new mathematical algorithms targeting one or more focus areas: (1) 3D+ reconstruction of structure/heterogeneity/dynamics from scattering and/or microscopy data; (2) autonomous analysis and decision-making for self-driving and/or human-in-the-loop experiments; (3) computer vision for extracting patterns, structure, and meaning from images and/or volumes; and (4) new mathematics and algorithms that enable new reliable new applications of machine learning and artificial intelligence to experimental data analysis
• Make advances in one or more of: multimodal data fusion/joint inference; uncertainty quantification with realistic noise/measurement error; complex physics- and artifact-aware forward modeling; and theory-grounded guarantees for proposed algorithms
• Collaborate with scientific users and experimentalists at DOE experimental facilities to apply the developed software to real datasets and meet their scientific needs
• Publish results in peer-reviewed venues, present at conferences/workshops, and contribute to CAMERA’s collaborative research activities

Skills

• Ph.D. in Applied Mathematics, Computer Science, Physics, or related field
• Strong research track record developing advanced mathematical and computational methods for analyzing complex experimental or imaging data
• Demonstrated expertise in several of the following areas: inverse problems, statistics, optimization, uncertainty quantification, and/or computer vision/machine learning
• Strong foundation in at least one of: numerical linear algebra, Fourier/spectral methods, scientific computing, and/or high-performance computing
• Proven ability to publish in peer-reviewed venues and present research at seminars, workshops, and scientific conferences
• Excellent written and verbal communication skills, with the ability to contribute effectively to large, collaborative, multidisciplinary projects in a diverse environment
• Familiarity with modern machine learning methods and software, including experience applying them to scientific or experimental datasets
• Experience collaborating with domain scientists to analyze real experimental data and translate scientific questions into robust, actionable computational approaches

Benefits

• Exceptional health and retirement benefits, including pension or 401K-style plans
• A culture where you’ll belong - we are invested in our teams!
• In addition to accruing vacation and sick time, we also have a Winter Holiday Shutdown every year
• Parental bonding leave (for both mothers and fathers)

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