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Scientific Co-Director, Data Science and Multimodal Integration
  • Job Number: 25522
  • Functional Area: Research - Scientific
  • Department: Koch Inst - Integrative Cancer Research
  • School Area: VP Research
  • Pay Range Minimum: $89,000
  • Pay Range Maximum: $173,000
  • Employment Type: Full-Time
  • Employment Category: Exempt
  • Visa Sponsorship Available: No
  • Schedule:
  • Pay Grade: No Grade


Posting Description

SCIENTIFIC CO-DIRECTOR, DATA SCIENCE AND MULTIMODAL INTEGRATION (DSMI), Koch Institute (KI), to direct the development and deployment of advanced data science and AI/ML methodologies in the KI community. This co-leader will maintain current understanding of data analyses and multimodal integration methods and develop novel methods leading to expanded DSMI services; review DSMI staff research output and direct deployment of new techniques; and significantly contribute to publications and grant applications. The leader will direct preparation of presentations for the community, lead development and presentation of training materials, and participate in mentorship activities.

Job Requirements
 REQUIRED: Ph.D. in computer science, computational biology, data science, or a related field with a strong emphasis on AI/ML applications; a minimum of seven years postdoctoral or equivalent research experience applying AI, machine learning (ML), and deep learning (DL) techniques to biological data; experience must include evidence of original or collaborative scholarship  documented by publications, software, or deployed intelligent systems; demonstrated expertise in statistical learning, ML, DL computer vision, and NLP methods applied to omics, imaging, and clinical datasets; programming skills with experience in ML/DL frameworks; data visualization and EDA expertise; multi-modal data integration and representation learning across diverse data types; and shared resource and leadership or supervisory experience. PREFERRED: Experience with data governance, FAIR principles, and regulatory compliance; knowledge of explainable AI (XAI) techniques and their application in biomedical contexts; experience with federated learning or other advanced AI paradigms; experience integrating AI models into pipelines; and familiarity with biomedical ontologies, knowledge graphs, or semantic data integration.

3/3/2026