Computational Imaging Postdoctoral Fellow (Cryo-EM)

Berkeley Lab’s (LBNL) Molecular Biophysics and Integrated Bioimaging (MBIB) is looking for a Postdoctoral Fellow to work on the development of computational methods and software for protein structure determination. 

In this exciting role, you will develop new mathematical approaches and computational algorithms to investigate protein structure and conformational heterogeneity from cryo-electron microscopy (cryo-EM) datasets, including single-particle imaging and in-situ cellular tomography. This research will focus on developing hybrid approaches that integrate deep generative models with classical optimization techniques, numerical linear algebra, and Fourier analysis. The key objectives include advancing optimization algorithms for the refinement of single-particle and subtomogram data to achieve improved structural resolution and interpretability. This position involves implementing and validating methods using both simulated and experimental datasets, organizing and presenting results to collaborators and the broader scientific community, and preparing manuscripts for publication in peer-reviewed journals.

The anticipated start date for this position is June 1, 2026, though an earlier start may be possible.

We’re here for the same mission, to bring science solutions to the world. Join our team and YOU will play a supporting role in our goal to address global challenges! Have a high level of impact and work for an organization associated with 17 Nobel Prizes!

Why join Berkeley Lab?

We invest in our employees by offering a total rewards package you can count on:

• 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)

What You Will Do:

• Develop and implement advanced computational algorithms for protein structure determination using cryo-electron microscopy including:
  • Generative modeling of conformational heterogeneity in electron microscopy.
  • Data-driven methods that use machine learning for reconstructing non-rigid structural variability.
  • Algorithms for automated alignment of single-particle and/or electron tomography data
  • theory and optimization techniques for tackling noise, missing data, and completion of models.
  • Automated pipelines for data analysis, structure completion, and model validation.
• Apply these methods to obtain high-resolution three-dimensional reconstructions of biomolecular structures from single-particle and cellular tomography data.
• Disseminate research outcomes through publications in high-impact, peer-reviewed journals and presentations at seminars, workshops, and international conferences.
• Maintain clear and well-organized documentation of theoretical developments, mathematical derivations, software implementations, and results.

• Contribute to the preparation of results, figures, and write-ups for research and grant proposals.

What is Required:

• A recent Ph.D. (within the last 1-2 years) in Computational Biophysics/Physics, Computational Cryo-Electron Microscopy, Applied Mathematics, Computer Science, or a closely related discipline.
• A solid background in Cryo-Electron Microscopy, with an emphasis on reconstruction workflows.
• Experience developing numerical methods for solving inverse problems in imaging including iterative reconstruction techniques, regularization methods, and uncertainty-aware modeling.
• Experience with generative models, variational inference, and physics-informed machine learning approaches.
• Experience coding in Python and PyTorch.
• Knowledge of numerical linear algebra, optimization techniques, and Fourier analysis as applied to imaging and inverse problems.
• Proven ability to design, implement, and evaluate computational algorithms for complex scientific problems.
• Strong organizational skills including experience maintaining detailed and accurate records of experiment results and analyzed data.
• Excellent oral and written communication skills including experience organizing/presenting technical reports and a record of publications in Cryo-Electron Microscopy or closely related fields.
• Demonstrated interpersonal and communication skills including experience conducting experiments independently and collaborating with an interdisciplinary research team. 

Desired Skills/Knowledge:

• Experience programming in C/C++/Fortran and use of modern version control tools.
• Familiarity with software development for high-performance computing environments, including MPI, openMP, and GPU-accelerated computing.
• Interest in extending scientific computing and generative modeling to new imaging modalities. 

Additional Information:

• Application Date: Priority consideration will be given to candidates who apply with a curriculum vitae (CV) or resume and a cover letter describing their interest in this position by March 6, 2026. Applications will be accepted until the job posting is removed.
• Appointment Type: This is a full time, exempt from overtime pay (monthly paid), 2 year (benefits eligible), Postdoctoral Fellow appointment with the possibility of renewal based upon satisfactory job performance, continuing availability of funds, and ongoing operational needs. You must have less than 3 years of paid postdoctoral experience. This position is represented by a union for collective bargaining purposes.
• Salary Range: The salary range for this position is $7,987 - $8,921 monthly / $95,844 - $107,052 annually and is expected to start at $7,987 monthly / $95,844 annually or above. Postdoctoral positions are paid on a step schedule per union contract and salaries are predetermined based on postdoctoral step rates. Each step represents one full year of completed post-Ph.D. postdoctoral and/or related research experience.
• Background Check: This position is 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 Modality: This position offers flexibility in work mode, including onsite, hybrid, full-time telework, or remote work, provided the individual resides within the contiguous United States. Onsite work will take place at Lawrence Berkeley National Lab, located at 1 Cyclotron Road, Berkeley, CA 94720. A REAL ID or other acceptable form of identification is required to access Berkeley Lab sites (for more information click here).

Want to learn more about working at Berkeley Lab? Please visit: careers.lbl.gov

Equal Employment Opportunity Employer: The foundation of Berkeley Lab is our Stewardship Values: Team Science, Service, Trust, Innovation, and Respect; and we strive to build community with these shared values and commitments. Berkeley Lab is an Equal Opportunity Employer. We heartily welcome applications from all who could contribute to the Lab's mission of leading scientific discovery, excellence, and professionalism. In support of our rich global community, all qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected categories under State and Federal law.

Berkeley Lab is a University of California employer. It is the policy of the University of California to undertake affirmative action and anti-discrimination efforts, consistent with its obligations as a Federal and State contractor.

Misconduct Disclosure Requirement: As a condition of employment, the finalist will be required to disclose if they are subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct, are currently being investigated for misconduct, left a position during an investigation for alleged misconduct, or have filed an appeal with a previous employer.