Associate Research Scientist - Biochemistry
- Brandeis University
- Location: Waltham, MA
- Job Number: 7313196 (Ref #: R0011970)
- Posting Date: Newly posted
- Application Deadline: Open Until Filled
Job Description
Department of Biochemistry, Brandeis University
Are you a creative scientist with a passion for integrating experimental and computational approaches? Do you thrive at the intersection of cutting-edge research, data science, and mentorship? Join the Street Lab at Brandeis University as we uncover mechanisms of molecular chaperones and build a new AI-powered scientific discovery tool.
Dr. Timothy Street’s lab investigates how Hsp70 and Hsp90 chaperones use ATP-driven conformational changes to regulate protein folding and degradation. By combining structural methods, protein folding, kinetic/thermodynamic analysis, and computational modeling, we aim to uncover the core principles of chaperone function.
We are now seeking an Associate Research Scientist to help lead an ambitious new initiative: building a structured, searchable experimental database and integrating it with an AI-driven research assistant to accelerate hypothesis generation, testing, and discovery.
Initial appointment is for two years, with potential for renewal.
Your Role Will Include:
Research & Mentorship (40%)
- Develop hands-on, modular training materials in experimental design and analysis
- Mentor and support undergraduate and graduate researchers
- Cultivate a collaborative learning environment across the lab
AI-Driven Mechanistic Discovery (40%)
- Help develop and train a “Street Lab AI model” to generate and test quantitative hypotheses
- Integrate experimental data into mechanistic models
- Contribute to high-impact publications through data analysis and manuscript writing
Data Infrastructure & Management (20%)
- Create and maintain a structured, user-friendly database of experimental results
- Ensure data is accessible for reanalysis, integration, and computational modeling
Qualifications:
- Ph.D. in biophysics, structural biology, biochemistry, or a related field
- 1–3 years of experience managing scientific datasets or databases
- Strong mentoring and communication skills
- Experience building quantitative mechanistic models from biochemical/biophysical data is preferred
- Familiarity with Python and scientific data analysis is a plus
- Previous experience with chaperone mechanisms is a plus
Are you a creative scientist with a passion for integrating experimental and computational approaches? Do you thrive at the intersection of cutting-edge research, data science, and mentorship? Join the Street Lab at Brandeis University as we uncover mechanisms of molecular chaperones and build a new AI-powered scientific discovery tool.
Dr. Timothy Street’s lab investigates how Hsp70 and Hsp90 chaperones use ATP-driven conformational changes to regulate protein folding and degradation. By combining structural methods, protein folding, kinetic/thermodynamic analysis, and computational modeling, we aim to uncover the core principles of chaperone function.
We are now seeking an Associate Research Scientist to help lead an ambitious new initiative: building a structured, searchable experimental database and integrating it with an AI-driven research assistant to accelerate hypothesis generation, testing, and discovery.
Initial appointment is for two years, with potential for renewal.
Your Role Will Include:
Research & Mentorship (40%)
- Develop hands-on, modular training materials in experimental design and analysis
- Mentor and support undergraduate and graduate researchers
- Cultivate a collaborative learning environment across the lab
AI-Driven Mechanistic Discovery (40%)
- Help develop and train a “Street Lab AI model” to generate and test quantitative hypotheses
- Integrate experimental data into mechanistic models
- Contribute to high-impact publications through data analysis and manuscript writing
Data Infrastructure & Management (20%)
- Create and maintain a structured, user-friendly database of experimental results
- Ensure data is accessible for reanalysis, integration, and computational modeling
Qualifications:
- Ph.D. in biophysics, structural biology, biochemistry, or a related field
- 1–3 years of experience managing scientific datasets or databases
- Strong mentoring and communication skills
- Experience building quantitative mechanistic models from biochemical/biophysical data is preferred
- Familiarity with Python and scientific data analysis is a plus
- Previous experience with chaperone mechanisms is a plus
