Protein Design Postdoc: ML-Driven Therapeutics

Visterra; Waltham (MA); On-site; USD 80,000 - 120,000; Full time

Job summary

Visterra seeks a creative Postdoctoral Research Fellow for the Protein Design team to shape early-stage drug discovery by applying cutting-edge protein design and machine learning methods to therapeutic challenges. You will work with antibody and non-antibody biotherapeutics, collaborating across computational and experimental groups.

The role is based in Visterra's Waltham, MA offices, with an expected 24-month fellowship length and a focus on advancing ML-driven structure prediction and de

Qualifications

• This is a Ph.D.-level research role requiring extensive protein design experience.
• Strong knowledge of experimental and computational protein structure/function and engineering.
• Experience employing or developing protein design methods.
• Proficiency with Python and machine learning frameworks for data analysis.

Responsibilities

• Champion the integration of cutting-edge ML methods into protein design workflows.
• Collaborate with computational and experimental scientists to drive design of therapeutic proteins and antibodies.
• Analyze results, troubleshoot methods, and propose creative solutions.
• Translate complex data analyses into clear findings for colleagues and leadership.
• Contribute to publication of novel methods and research findings.

Skills

Python

ML libraries

Education

Ph.D. or equivalent in computational/structural biology/biochemistry

Tools

PyRosetta

AlphaFold

ESMFold

RFdiffusion

ProteinMPNN

BindCraft

PyMOL

Job description

Visterra seeks a creative Postdoctoral Research Fellow for the Protein Design team to shape early-stage drug discovery by applying cutting-edge protein design and machine learning methods to therapeutic challenges. You will work with antibody and non-antibody biotherapeutics, collaborating across computational and experimental groups.

The role is based in Visterra's Waltham, MA offices, with an expected 24-month fellowship length and a focus on advancing ML-driven structure prediction and de