General Information
Position: Postdoctoral Scholar
Organization: The Molecular Foundry, Lawrence Berkeley National Laboratory
Location: Berkeley, California, USA
Type: Full time
Remote Work: No
Job Description
The research groups of Staff Scientists Emory Chan and Samuel Blau at Berkeley Lab are seeking a postdoctoral scholar to build and integrate leading-edge robotic and ML capabilities enabling autonomous synthesis of colloidal nanocrystals guided by both high-performance computation and deep learning.
The Postdoc will lead efforts to adapt an existing robotic nanocrystal synthesis platform, Monte Carlo simulations, and graph neural networks to perform closed-loop discovery of lanthanide-doped upconverting and photon avalanching nanoparticle heterostructures for emerging applications in super-resolution imaging, optical computing, and additive manufacturing.
###Duties/Responsibilities
- Develop a Python-based pipeline for closed-loop control of an existing Freeslate CM3 nanoparticle synthesis robot (HERMAN) and associated characterization tools and databases.
- Physically and digitally integrate an in-line laser spectroscopy module into the synthesis robot workflow.
- Integrate into the workflow a machine learning module that uses both prior experimental results and computational data to direct future robotic experiments and high-throughput simulations.
- Demonstrate autonomous nanoparticle discovery on lanthanide-doped upconverting nanoparticle heterostructures.
- Characterize the properties of synthesized materials using techniques including optical spectroscopy, microscopy, XRD, and TEM.
- Analyze large datasets and develop mechanistic or statistical models that explain observations.
- Publish results in high-impact journals and communicate results at international conferences.
Qualifications
Essential:
- Ph. D. in Chemistry, Chemical Engineering, Physics, Materials Science, Data Science, Computer Science, or a related field.
- 1+ year experience programming in Python or comparable computer language. Candidate should be able to design the necessary algorithms, data structures, and interfaces to accomplish a desired task, then successfully write, comment, test, and debug a program, given a desired behavior and no pre-existing codebase.
- Exposure to basic concepts of machine learning.
- Exposure to computer control of instruments.
- Basic experience with chemical synthesis (inorganic, organic, organometallic, polymer, materials)
- Experience characterizing the physical properties of materials, such as with optical spectroscopy.
- Ability to quickly learn new scientific concepts, software, and hardware skills.
- Excellent verbal and written communication skills.
- Ability to maintain accurate, detailed, and clear experimental records.
- Proactive in identifying, implementing, and following safety protocols.
- Strong record of publication in peer-reviewed journals or conference proceedings.
Desirable:
- Experience with machine learning – e.g. constructing and splitting datasets, model training, architectural development
- Experience constructing or automating instruments
- Expertise in synthesis of colloidal nanoparticles
- Expertise in robotics, laboratory automation, or high-throughput chemistry
- Expertise in high performance computing or simulation
Compensation and Benefits
Salary and benefits are determined by collective bargaining agreement based on experience and are highly competitive.
Application Process
How to Apply: Interested candidates should send a cover letter and CV to EMChan@lbl.gov
Additional Information
Additional information about the Chan group can be found at https://combinano.lbl.gov
Examples of our recent research in this topic area include:
ChemRxiv 2025, DOI: 10.26434/chemrxiv-2024-1dw4q-v2
Nature Photonics 2025, 19, 212–218, DOI: 10.1038/s41566-024-01577-x
Nature 2023, 618, 951–958, DOI: 10.1038/s41586-023-06076-7
Nano Letters 2023, 23, 11129-11136, DOI: 10.1021/acs.nanolett.3c03568
Nature 2021, 589, 230–235, DOI: 10.1038/s41586-020-03092-9