Details of Research Project:
develop and apply machine learning models for antibody design and engineering focusing on sequencestructure relationships binding prediction and optimization of antibody variants. The project integrates computational modeling with experimental protein engineering and immunological validation.
Technical Duties: (include any protocols)
Develop train and benchmark machine learning models for antibody sequence and structure analysis
Perform large-scale computational analysis of antibody and nanobody repertoires
Integrate structural modeling tools with ML-based prediction pipelines
Assist with experimental validation workflows including recombinant protein expression and binding assays
Maintain documentation of computational workflows (HPC) and research protocols
1. Ongoing evaluation and treatment or research under the direct supervision of an M.D. or Ph.D.
2. Neuropsychological assessments if appropriate to position.
3. Cognitive remediation
4. Individual supportive therapy.
5. Group supportive therapy.
6. Interdisciplinary team participation and coordination.
7. Co-lead training seminars.
Educational and other Requirements for the position: PhD
Experience Required:
Experience in machine learning or statistical modeling
Familiarity with protein structure antibody engineering or computational biology
Experience with Python or similar scientific programming languages
Ability to work collaboratively in an interdisciplinary research environment
Goals/Outcomes of the Research Project:
Establish robust machine learning frameworks to support antibody and nanobody engineering
Generate validated computational tools that improve antibody design efficiency and accuracy
Produce publishable research findings and computational resources to advance protein engineering efforts
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