Johns Hopkins, founded in 1876, is America's first research
university and home to nine world-class academic divisions working
together as one university.
Are you excited to explore a yet-to-be-solved problem at the heart
of AI-based protein drug design? Join Jeffrey Gray’s academic lab
collaboration with an industry leader to develop and mature some of
the most in-demand research skills in academia and industry
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. We have
recently established partnerships with leading pharmaceutical
companies that allow us to test our new methods experimentally.
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. Your designs will be
experimentally tested by our partners. You will be a part of a
dynamic, collaborative, and inclusive research group.
- Highly competitive salary (up to $80,000 stipend) goes far in
- Full benefits including health insurance (coverage for eye and
- The Gray laboratory is part of the Rosetta Commons (Prof. Gray
serves as Co-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
- State-of-the-art computing infrastructure (NVIDIA A100 GPUs and
CPUs for seamless computing).
- Supportive mentoring environment, with regular 1:1 meetings and
Individual Development Plans every 6 months.
More information about the Gray lab at http://graylab.jhu.edu
Key responsibilities could include, but are not limited to,
assisting in the following:
- Review literature on antibody design technologies and deep
learning approaches to antibodies.
- Assess gaps in current antibody design tools.
- Assess of emerging deep learning methods for application in
- Create of novel deep learning tools for antibody engineering,
including assembling novel data sets, building new architectures,
training and assessing models.
- Critically assess of antibody 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
- PhD in Engineering, biophysics, computational biology, or
- Experience with scientific algorithm development, protein
biophysics or engineering, protein structure prediction and
- Experience in solving complex scientific or engineering
- Excellent writing and communication skills
- Ability to work in diverse teams and contribute to an inclusive
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
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