Job ID:
253233
Postdoctoral Scholar
University of Kentucky
We are seeking a highly motivated postdoctoral researcher to join the Zero Knowledge Discovery Lab (zedlab), to work on problems at the intersection of biology, medicine, mathematics and computation. The successful candidate will contribute to the development of next-generation learning algorithms to understand human health trajectories through advanced digital twin frameworks. This position offers an extraordinary opportunity in the field of computational health modeling that integrates clinical, genotypic, environmental, and behavioral data at scale to devise next generation healthcare tools. The ideal candidate will be an applied mathematician with equally sophisticated skill in theorem proving, algorithms and coding. We are seeking a highly motivated postdoctoral researcher to join the Zero Knowledge Discovery Lab (zedlab), to work on problems at the intersection of biology, medicine, mathematics and computation. The successful candidate will contribute to the development of next-generation learning algorithms to understand human health trajectories through advanced digital twin frameworks. This position offers an extraordinary opportunity in the field of computational health modeling that integrates clinical, genotypic, environmental, and behavioral data at scale to devise next generation healthcare tools. The ideal candidate will be an applied mathematician with equally sophisticated skill in theorem proving, algorithms and coding.
Key Responsibilities:
- Mathematical Foundations: Work on the rigorous mathematical foundations of emerging digital twin frameworks, and the theoretical aspects of ML.
- Research & Algorithm Development: Develop advanced algorithms to predict complex health trajectories, simulating individual patient journeys, and providing real-time dynamic views of patient health. This involves working with multimodal datasets, including EHRs, genetic data, and social determinants of health.
- Collaborative Research: Engage with a diverse group of researchers in fields such as computational biology, medicine, and social sciences. Publish findings in top-tier journals, present at conferences, and contribute to the advancement of the field.
Requirements:
- Theorem-proving Background: A strong foundation in mathematical modeling, particularly stochastic processes, information theory and discrete mathematics.
- Programming Skills: Proficiency in Python, TensorFlow and scikit-learn, experience in C++
- Documented Productivity: Record of peer-reviewed publications
- Interdisciplinary Interest: Candidates should have a passion for applying computational techniques to solve complex problems across disciplines.
Compensation and Benefits:
- Competitive Salary: Funding is available for a competitive salary, based on qualifications and experience.
- Research Support: Additional funding will be provided for supplies and travel to conferences.
- Professional Development: Opportunities for significant career growth, networking with leading researchers, and engaging in high-impact interdisciplinary projects.
- Women and underrepresented groups are encouraged to apply.
A complete application will include a link to Github Profile and 1-2 reference contact information uploaded as Specific Request 1.
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