Senior Bio Machine Learning Scientist - 136085
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![]() United States, California, San Diego | |
![]() 9500 Gilman Drive (Show on map) | |
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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 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. The Ideker Laboratory is recruiting a Senior BioMachine Learning (BioML) Scientist to lead machine learning R&D 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. Under direction, the Senior BioMachine Learning Scientist will oversee AI/ML research projects and objectives toward the successful completion of the APRA-H ADAPT program goals. This role will require expertise in bioinformatics, machine learning, systems biology, and project leadership, with a deep understanding of modern cancer biology. In particular, this position will:
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, conferences and committees. Assists in the design, implementation and recommends new hardware and software technologies for advancing complex HPC, data science, CI projects. May lead a team of research and technical staff. MINIMUM QUALIFICATIONS
Pay Transparency Act Annual Full Pay Range: $119,400 - $230,800 (will be prorated if the appointment percentage is less than 100%) Hourly Equivalent: $57.18 - $110.54 Factors in determining the appropriate compensation for a role include experience, skills, knowledge, abilities, education, licensure and certifications, and other business and organizational needs. The Hiring Pay Scale referenced in the job posting is the budgeted salary or hourly range that the University reasonably expects to pay for this position. The Annual Full Pay Range may be broader than what the University anticipates to pay for this position, based on internal equity, budget, and collective bargaining agreements (when applicable). |