Postdoctoral Position in Jeffrey J. Gray's Computational Protein Structure and Design Lab

General Description

Are you excited to explore a yet-to-be-solved problem at the heart of AI-based protein engineering? Join Jeffrey Gray's research lab to develop and mature some of the most in-demand research skills in academia and industry today. New approaches in deep neural networks are revolutionizing the study of protein structure, but so far these advances have not solved the critical issues to be able to design new therapeutic antibodies from scratch.

The Gray lab is a leader in the study of protein-protein interactions and computational modeling of antibodies, including emerging deep learning approaches. This position will allow you to learn the emerging deep learning approaches to proteins and antibodies and craft new machine learning approaches for antibody engineering, protein-protein docking, and carbohydrate modeling. We create new methods and collaborate with academics and industry to apply these methods to important biological problems and to therapeutics.

You will be a part of a dynamic, collaborative, and inclusive research group.

More information about the Gray lab at (Use the "Apply for this Job" box below)..edu

• Highly competitive salary (up to $80,000 stipend) goes far in affordable Baltimore.
• Full benefits including health insurance (coverage for eye and dental insurance).
• The Gray laboratory is part of the Rosetta Commons (Prof. Gray serves as Director), and so you will be able to participate in Rosetta Conferences, workshops, and outreach.
• Johns Hopkins offers an unmatched environment for life sciences research, with a dynamic community in biomolecular engineering, biophysics, biochemistry, and medicine.
• Lab alumni have advanced to positions in industry giants (Genentech, Merck, Pfizer, GSK), and successful recent startups (Generate Bio, Outpace Bio).
• Initial appointment period of one year, with expectation of 2-4 years total.
• State-of-the-art computing infrastructure (NVIDIA H100 and A100 GPUs and CPUs for seamless computing).
• Supportive mentoring environment, with regular 1:1 meetings and Individual Development Plans every 6 months.

• Review literature on biomolecular structure and design technologies and deep learning approaches.
• Assess gaps in current tools.
• Assess emerging deep learning methods for application in biomolecular engineering.
• Create novel deep learning tools for biomolecular engineering, including assembling novel data sets, building new architectures, training and assessing models.
• Critically assess biomolecular designs using multiple deep learning tools, physics-based models, and structural analysis.
• Plan collaboration with experimentalists, to best assess competing design strategies.
• Write journal articles and presentations for sharing the outcomes.

• PhD in Engineering, biophysics, computational biology, or related field.
• Experience with scientific algorithm development, protein biophysics or engineering, protein structure prediction and design.
• Experience in solving complex scientific or engineering problems.
• Excellent writing and communication skills.
• Ability to work in teams and contribute to a supportive and collaborative work culture.

In Interfolio, applicants should upload a CV, a brief statement of purpose, a code sample, and 2-3 representative publications (preprints acceptable). The statement should state why the Gray lab is a good fit for your career trajectory. The code sample can be a file or a few files or a link (e.g. a Github repository) that demonstrates your coding level. Applications will be considered until the positions are filled.

Johns Hopkins University fosters intellectual interaction, provides state-of-the-art resources, and embraces diversity in our workforce. The Johns Hopkins University is an EEO/AAA Employer. To apply for this position, visit

The referenced salary range represents the minimum and maximum salaries for this position and is based on Johns Hopkins University's good faith belief at the time of posting. Not all candidates will be eligible for the upper end of the salary range. The actual compensation offered to the selected candidate may vary and will ultimately depend on multiple factors, which may include the successful candidate's geographic location, skills, work experience, internal equity, market conditions, education/training and other factors, as reasonably determined by the University.

Johns Hopkins offers a total rewards package that supports our employees' health, life, career and retirement. More information can be found at .

The Johns Hopkins University is committed to equal opportunity for its faculty, staff, and students. To that end, the university does not discriminate on the basis of sex, gender, marital status, pregnancy, race, color, ethnicity, national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status or other legally protected characteristic. The…