Job Description
Department of Biochemistry has an outstanding opportunity for Research Scientist/Engineer 3 to join their team.
About this Opportunity
This interdisciplinary role operates at the intersection of de novo protein design, regenerative medicine, and synthetic biology. By integrating AI-guided computational protein engineering with genomics and molecular biology, the position aims to decode and manipulate the cellular signaling pathways that govern stem cell identity, tissue development, and aging.
Purpose of the research project(s) this position supports:
To develop transformative, AI-driven protein technologies that enable precise, programmable control over cellular identity and function. By engineering de novo signaling molecules and synthetic regulatory systems, these projects aim to orchestrate cell fate specification, transdifferentiation, vascular regeneration, and tissue repair. This work bridges fundamental developmental biology with translational engineering, establishing versatile therapeutic modalities designed to restore damaged tissues and reverse historically intractable human diseases.
Duties and Responsibilities:
1. Computational Protein Design & Engineering (30%)
Architect, model, and optimize novel signaling proteins ("Novokines") utilizing state-of-the-art, AI-guided de novo design suites (e.g., RFdiffusion, ProteinMPNN, and AlphaFold-based structure prediction).
Iterate and refine designs based on empirical wet-lab feedback to maximize binding specificity, potency, and pathway selectivity.
2. Experimental Validation in Stem Cell & Aged Fibroblast Systems (25%)
Cultivate, differentiate, and immunophenotypically characterize stem cells and aged fibroblasts to evaluate Novokine-mediated cell-state transitions, including lineage specification, transdifferentiation, and vascular morphogenesis.
Orchestrate and execute robust functional assays, including reporter assays, lineage tracing, and downstream signaling pathway readouts.
3. Genomic & Systems-Level Analysis (15%)
Execute and analyze transcriptomic, epigenomic, and single-cell sequencing (scRNA-seq) workflows to map the molecular mechanisms driving engineered cell-fate transitions.
Integrate multi-omic datasets to uncover predictive biological insights, refining iterative protein design architectures.
4. Data Science & Systems Biology Modeling (10%)
Deconstruct complex experimental and sequencing datasets using advanced statistical techniques; deploy or build computational systems biology models to decipher signaling network dynamics.
5. Scientific Communication, Manuscripts, & Grants (10%)
Draft and co-author high-impact manuscripts for peer-reviewed publication.
Contribute technical writing and preliminary data visualization to grant proposals, progress reports, and abstract submissions for national/international conferences.
6. Mentorship & Interdisciplinary Collaboration (5%)
Provide technical mentorship to junior trainees, graduate students, and research technicians in computational workflows and molecular biology techniques.
Manage collaborative data and reagent exchanges with cross-functional internal and external academic/industry partners.
7. Regulatory Compliance & Laboratory Governance (5%)
Maintain meticulous records within electronic laboratory notebooks (ELNs).
Ensure strict adherence to institutional biosafety, IACUC, and IRB protocols.
Required Qualifications
To be considered for this opportunity your application must demonstrate you meet both the minimum qualifications and additional qualifications listed below. Equivalent education and/or experience may substitute for minimum qualifications except when there are legal requirements, such as a license,
certification, and/or registration.
Minimum Requirements:
Applicants who do not meet these qualifications WILL NOT be forwarded to the Hiring Department.
Bachelor's Degree in Molecular and Human Genetics and 4 years of experience in cell and molecular biology and AI/ML-based protein work.
Additional Requirements:
1. Strong foundational knowledge in molecular and cellular biology, including gene expression, protein function, and signal transduction pathways.
2. Experience with standard laboratory techniques such as mammalian cell culture, molecular cloning, plasmid construction, and transfection/transduction methods.
3. Familiarity with protein expression systems, protein purification approaches, and/or biochemical assay development.
4. Experience or strong interest in AI-assisted protein engineering, including de novo protein design, computational protein modeling, and structure-guided design approaches.
5. Experience with AI/ML-based protein-protein interaction prediction methods, including network inference, structural modeling, or sequence-based interaction prediction approaches.
6. Familiarity with computational or statistical approaches for cell state identification, including analysis of single-cell or high-dimensional data (e.g., clustering, trajectory inference, or classification of cellular states).
7. Ability to design, execute, and troubleshoot experiments independently with strong attention to detail and reproducibility.
8. Experience with microscopy-based assays and quantitative image analysis (preferred).
9. Working knowledge of basic data analysis and visualization tools (e.g., GraphPad Prism, R, or Python preferred).
10. Understanding of experimental design, statistical analysis, and proper controls in biological experiments.
11. Strong organizational skills, including maintaining accurate laboratory records and documenting experimental workflows.
12. Ability to work collaboratively in a multidisciplinary team environment and communicate results effectively.
13. Strong written and verbal communication skills for presenting data, preparing reports, and contributing to manuscripts or grant-related documents.
14. Ability to learn new techniques quickly and adapt to evolving research priorities in a fast-paced research environment.
Preferred Qualifications:
1. Strong background in AI/ML-based de novo protein design, protein-protein interaction prediction, including sequence-based modeling, structural docking approaches, or network-based inference methods.
2. Experience with computational biology approaches for cell state identification, including single-cell RNA-seq analysis, clustering, trajectory inference, and classification of heterogeneous cellular populations.
3. Experience in transdifferentiation.
Compensation, Benefits and Position Details
Pay Range Minimum:
$80,244.00 annual
Pay Range Maximum:
$82,800.00 annual
Other Compensation:
Benefits:
For information about benefits for this position, visit https://www.washington.edu/jobs/benefits-for-uw-staff/
Shift:
First Shift (United States of America)
Temporary or Regular?
This is a regular position
FTE (Full-Time Equivalent):
100.00%
Union/Bargaining Unit:
UAW Research
