Director of Bio Machine Learning

UCSD Layoff from Career Appointment:
Apply by 09/12/2025 for consideration with preference for rehire. All layoff applicants should contact their Employment Advisor.

Special Selection Applicants:
Apply by 09/23/2025. Eligible Special Selection clients should contact their Disability Counselor for assistance.

The qualifications of this posting have been slightly modified as of 9/09/2025

This position has recently been accreted by UAW RP union and will be a part of that union moving forward.

The Ideker Laboratory at UCSD is recruiting a Director of Bio Machine Learning (BioML) to lead R&D efforts for the ADAPT project, a new precision cancer therapy initiative funded by ARPA-H. The overall goal of the ADAPT project is to use advanced AI/ML technologies to deliver the right therapy to the right patients at the right time. ADAPT will revolutionize cancer treatment by using predictive biomarkers and interpretable AI/ML to create dynamical cancer treatment strategies and personalized therapies for patients with metastatic cancers.

The Director of Bio Machine Learning will oversee AI/ML research projects and objectives toward the successful completion of the APRA-H ADAPT program goals, including providing direction and support to the team. This role will require expertise in computer science, bioinformatics, AI/deep learning, systems biology, modern cancer biology, and project leadership. In particular, this position will:

• Lead the design and implementation of deep learning approaches for drug recommendation in cancer.

• Lead interdisciplinary teams by collaborating with biologists, oncologists, statisticians, and other computational/ML scientists to translate computational findings into therapeutic strategies.

• Design, debug, and optimize algorithms and computational techniques to analyze and fuse complex, multimodal datasets, including genomic, transcriptomic and proteomic data from various sources for biomarker discovery and therapy recommendation.

• Create and validate computational tools to track tumor changes and adapt therapy plans in real-time using insights from clinical data.

• Create and oversee a central benchmarking platform for standardizing AI/ML models.

• Contribute to the creation and maintenance of a central cancer treatment and analysis platform, ensuring accurate and timely data availability for clinicians and researchers.

• Provide mentorship and guidance to junior researchers, including PhD students and postdocs, fostering expertise in bioML and its applications in oncology.

• Prepare detailed reports, publications, and presentations showcasing progress on ADAPT program goals, and represent the lab at national and international conferences.

• Collaborate closely with the Ideker Lab's Program Director to identify and pursue new funding opportunities, align lab objectives with emerging trends in precision medicine, and contribute to grant proposals to support long-term research initiatives.

• In addition to the main ADAPT initiative, we expect that this position will work on and potentially lead other projects within the Ideker Lab research portfolio as needed.

About the Ideker Laboratory:
The Ideker Laboratory is in the Division of Human Genetics and Precision Medicine at UC San Diego School of Medicine. We are a vibrant research team of 30-40 staff, postdocs, graduate students, and undergraduate students known for its dynamic and collaborative environment. We perform bioinformatics research coupled with wet-lab investigations, working in the areas of network biology, data-driven hierarchical modeling, and machine learning applied to biomedicine.

We also support multiple open-source software projects, some with 100K+ users. One of the main research goals of the Ideker Lab is to create artificially intelligent, mechanistic models of cancer for translation of patient data to precision diagnosis and treatment. We are advancing this goal by developing "visible" predictive machine learning approaches to model the flow of genetic information to translate genotype to phenotype and, importantly, to identify the molecular functions and mechanisms by which these predictions are made.

Additional Information:
Applies advanced computational, computer science, data science, and CI software research and development principles, with relevant domain science knowledge where applicable, to perform highly complex research, technology and software development which involve in-depth evaluation of variable factors impacting medium to large projects of broad scope and complexities. Designs, develops, and optimizes components / tools for major HPC / data science / CI projects.

Resolves complex research and technology development and integration issues. Gives technical presentations to associated research and technology groups and management. Evaluates new hardware and software technologies for advancing complex HPC, data science, CI projects. May represent the organization as part of a team at national and international meetings,…