Postdoctoral Position to explore Antibody-Design based on Artificial Intelligence (AI) with leading pharma partner

Requisition #: A-126836-3

Status: Full Time

Type: Fellowship

School: Whiting School of Engineering

Department/Program: Department of Chemical and Biomolecular Engineering

Location City: Baltimore

Location State: MD

Location Zip Code: 21218

Closing Date: Open until filled

General Description

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 in collaboration with an industry leader to develop and mature some of the most in-demand research skills in academia and industry today. 

[This posting is slightly dated but I continue to accept applications through this portal; our lab research continues to revolve around many themes in this posting. - JJG 2024]

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 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 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 years total.

  • 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 antibody engineering.
  • 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 outcomes.

Qualifications

  • 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 diverse teams and contribute to an inclusive work culture

Application Instructions

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: apply.interfolio.com/126836

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 university is committed to providing qualified individuals access to all academic and employment programs, benefits and activities on the basis of demonstrated ability, performance and merit without regard to personal factors that are irrelevant to the program involved.

Pre-Employment Information

The successful candidate(s) for this position will be subject to a pre-employment background check.

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