Postdoctoral Fellow, AI for Quantitative Medicine
Pfizer
9 hours ago
Remote friendly (Cambridge, MA)
United States
Clinical Research and Development
What You Will Achieve
- Scientific Leadership in AI-Enabled Biomarker Development: Develop and validate advanced AI models (e.g., deep learning–based imaging biomarkers) to quantify and predict tumor response from longitudinal clinical trial data.
- Integration of Multimodal Clinical Data: Integrate medical imaging, PK/PD, and clinical outcome data to establish mechanistic and predictive relationships between early response dynamics and downstream survival outcomes.
- Developing Novel Quantitative Modeling Approaches: Explore empirical and semi-mechanistic modeling approaches integrating AI-enabled tumor response data with long-term clinical outcomes.
- Innovation in Early Response Endpoints: Conceptualize and evaluate novel, continuous early response endpoints that address limitations of conventional criteria (e.g., RECIST), focusing on clinical relevance and robustness.
- Translational Impact on Drug Development Decisions: Translate methods into scalable frameworks to support dose optimization, trial design, and early efficacy decision-making across oncology programs.
- Cross-Functional and External Collaboration: Collaborate with internal oncology and data/AI partners and external technology/academic collaborators.
- Scientific Rigor and Reproducibility: Ensure rigorous validation and documentation to support adoption, regulatory interactions, and reuse.
- Knowledge Dissemination and Thought Leadership: Present results internally and publish/contribute to external scientific venues.
Here Is What You Need (Minimum Requirements)
- PhD (or equivalent) in a relevant quantitative field (e.g., biomedical/chemical engineering, computational biology, biostatistics, mathematics, physics, computer science, pharmaceutical sciences).
- Research experience applying machine learning/deep learning to biomedical, imaging, PK/PD, or clinical data.
- Proficiency in at least one programming language (e.g., Python, R, MATLAB).
- Experience with ML/deep learning (PyTorch, TensorFlow), medical image analysis, and/or pharmacometrics.
- Strong foundation in deep learning (e.g., CNNs, Transformers, vision-language or multimodal models).
- Experience with large complex datasets; model development, validation, and performance evaluation.
- Ability to translate innovations into practical insights in regulated/applied research environments.
- Strong written and verbal communication skills.
Bonus Points If You Have (Preferred Requirements)
- Oncology, medical imaging, or clinical trial PK/PD analysis experience.
- Familiarity with tumor response frameworks (e.g., RECIST) and limitations.
- Experience integrating imaging with clinical outcomes, PK/PD, or survival analysis.
- Publication/presentation track record.
- Collaboration experience with industry/translational teams.
- Interest in AI methods that impact drug development strategy and clinical decision-making.
Additional Information
- Relocation support available
- Work Location Assignment: Hybrid
Benefits
- Annual base salary range: $64,600.00–$107,600.00; eligible for participation in Global Performance Plan with 7.5% bonus target.
- 401(k) with matching contributions; additional retirement savings contribution; paid vacation/holidays/personal days; paid caregiver/parental and medical leave; medical, prescription drug, dental, and vision coverage.
Application Instructions
- Not provided in the text.
- Scientific Leadership in AI-Enabled Biomarker Development: Develop and validate advanced AI models (e.g., deep learning–based imaging biomarkers) to quantify and predict tumor response from longitudinal clinical trial data.
- Integration of Multimodal Clinical Data: Integrate medical imaging, PK/PD, and clinical outcome data to establish mechanistic and predictive relationships between early response dynamics and downstream survival outcomes.
- Developing Novel Quantitative Modeling Approaches: Explore empirical and semi-mechanistic modeling approaches integrating AI-enabled tumor response data with long-term clinical outcomes.
- Innovation in Early Response Endpoints: Conceptualize and evaluate novel, continuous early response endpoints that address limitations of conventional criteria (e.g., RECIST), focusing on clinical relevance and robustness.
- Translational Impact on Drug Development Decisions: Translate methods into scalable frameworks to support dose optimization, trial design, and early efficacy decision-making across oncology programs.
- Cross-Functional and External Collaboration: Collaborate with internal oncology and data/AI partners and external technology/academic collaborators.
- Scientific Rigor and Reproducibility: Ensure rigorous validation and documentation to support adoption, regulatory interactions, and reuse.
- Knowledge Dissemination and Thought Leadership: Present results internally and publish/contribute to external scientific venues.
Here Is What You Need (Minimum Requirements)
- PhD (or equivalent) in a relevant quantitative field (e.g., biomedical/chemical engineering, computational biology, biostatistics, mathematics, physics, computer science, pharmaceutical sciences).
- Research experience applying machine learning/deep learning to biomedical, imaging, PK/PD, or clinical data.
- Proficiency in at least one programming language (e.g., Python, R, MATLAB).
- Experience with ML/deep learning (PyTorch, TensorFlow), medical image analysis, and/or pharmacometrics.
- Strong foundation in deep learning (e.g., CNNs, Transformers, vision-language or multimodal models).
- Experience with large complex datasets; model development, validation, and performance evaluation.
- Ability to translate innovations into practical insights in regulated/applied research environments.
- Strong written and verbal communication skills.
Bonus Points If You Have (Preferred Requirements)
- Oncology, medical imaging, or clinical trial PK/PD analysis experience.
- Familiarity with tumor response frameworks (e.g., RECIST) and limitations.
- Experience integrating imaging with clinical outcomes, PK/PD, or survival analysis.
- Publication/presentation track record.
- Collaboration experience with industry/translational teams.
- Interest in AI methods that impact drug development strategy and clinical decision-making.
Additional Information
- Relocation support available
- Work Location Assignment: Hybrid
Benefits
- Annual base salary range: $64,600.00–$107,600.00; eligible for participation in Global Performance Plan with 7.5% bonus target.
- 401(k) with matching contributions; additional retirement savings contribution; paid vacation/holidays/personal days; paid caregiver/parental and medical leave; medical, prescription drug, dental, and vision coverage.
Application Instructions
- Not provided in the text.