
Postdoctoral Fellow, Protein Adsorption
- San Francisco, CA
- $73,000-138,500 per year
- Permanent
- Full-time
- Develop and execute research strategies to evaluate protein adsorption at interfaces (e.g. solid-liquid, liquid-air, oil-water).
- Perform in silico and experimental characterizations of proteins, solutions, and interfaces, including surface charge, hydrophobicity, and structural properties, using techniques like quartz crystal microbalance with dissipation (QCM-D), surface tension measurements, zeta potential, dynamic light scattering (DLS), reverse-phase HPLC (RP-HPLC), and FTIR.
- Apply machine learning techniques to enhance the predictive model for protein adsorption.
- Collaborate effectively with cross-functional teams comprising experts in AI/ML, computational modeling, structural biology, analytical characterization, and formulation development.
- Proactively seek out new information in the literature and incorporate this into individual project(s) as well as the overall program.
- Publish findings in peer-reviewed journals and present at scientific conferences to disseminate research outcomes.
- Maintain a high level of productivity and adhere to scientific standards and safe lab practices.
- PhD in Biochemistry, Biophysics, Chemical Engineering, or a related field (summer and fall graduates are also welcome to apply).
- Strong background in protein chemistry, interfacial dynamics, and modeling.
- Proven ability to independently design and conduct experiments, analyze data, and develop follow-up strategies.
- Excellent project management skills with the ability to multitask and meet timelines.
- Demonstrated scientific writing and strong verbal communication skills.
- Global mindset to thrive in a diverse culture and environment.
- Experience with QCM-D, surface tension measurements, homology modeling, and other experimental protein characterization techniques (size exclusion chromatography, reverse-phase HPLC, dynamic light scattering, zeta potential, small angle X-ray scattering).
- Advanced computational and data analysis skills in biological systems.
- Familiarity with high-throughput experimental techniques and AI/ML applications in research.
- Builds strong relationships with peers and cross functionally with partners outside of team to enable higher performance.
- Learns fast, grasps the "essence" and can change course quickly where indicated.
- Raises the bar and is never satisfied with the status quo.
- Creates a learning environment; open to suggestions and experimentation for improvement.
- Embraces the ideas of others, nurtures innovation, and manages to reality.