AI-Enabled Biology and Medicine - A Division-wide, Open-rank Faculty Search

AI-Enabled Biology and Medicine - A Division-wide, Open-rank Faculty Search

Salary Range:
PhD: $140,000 - $350,000, dependent on experience and academic rank; MD or MD/PhD: $170,000 - $475,000, dependent on experience, clinical expectations, and academic rank

The University of Chicago's Biological Sciences Division is searching for full-time scientists, physician-scientists, and engineers who
bring innovative experimental, computational, and theoretical approaches to bear on fundamental questions in biology, medicine, and public health
. This search is focused on realizing the power and
promise of AI and statistical modeling approaches
for biological sciences, capturing the full continuum from basic biological discovery to clinical translation and public health, including programs that bridge biophysics, computation, systems biology, bioengineering, and medicine.

We especially welcome applicants who develop innovative approaches to investigate biological complexity and emergent properties at any scale—from biomolecular design, molecular systems, and cellular physiology to cellular communities in tissues, organisms, and ecosystems. We welcome applicants who use quantitative and AI-driven methods to generate mechanistic insight, develop interpretable and predictive models, advance therapeutic and translational strategies for disease diagnosis, treatment, and management, or to develop new theory for biology.

Academic rank, track, tenure status, and compensation are dependent upon qualifications.

Primary appointments will be in appropriate departments or units, most likely in the Biological Sciences Division (BSD), though potentially in the Physical Sciences Division (PSD) or Pritzker School of Molecular Engineering (PME), with possible secondary appointments in other relevant units depending on the candidates' research.

These positions are benefits eligible.

The University of Chicago offers a wide range of benefits programs and resources for eligible employees, including health, retirement, and paid time off. Information about the benefit offerings can be found in the Benefits Guidebook.

Appointeeswill join a highly collaborative academic ecosystem in which outstanding basic science and clinical departments are co-located on a compact urban campus, with state-of-the-art core facilities, exceptional graduate and medical students, and opportunities to participate in multidisciplinary research, translation, commercialization, and entrepreneurship, including Hyde Park Labs, a new hybrid facility for academic research, science-based companies and the UChicago Science Incubator.

The goal of this search is to recruit a cluster of faculty who will establish a distinctive community in AI-Enabled biology and medicine, forging new conceptual frameworks that predict, explain, and engineer biology across scales and with transformative consequences for both fundamental discoveries and applications to human health.

• Computational and experimental approaches that yield interpretable, mechanistic models of living systems, from molecules to cell signaling networks, to host-microbiome symbioses, organismal behavior and ecosystems
• Research aimed to discover the evolutionary origins of functional biological systems at any scale.
• Spatial and temporal biology through interpretable, multimodal representations of biological organization and dynamics across scales
• Digital twins - Generative, predictive representations of cells, networks, and tissues that forecast dynamics such as morphogenesis, tumor growth, neural computation, organismal behavior and cognition, immune-microbiome interactions, and regeneration
• Machine-learning foundations for multimodal integration of data across genomics, metagenomics, proteomics, metabolomics, imaging, clinical records, and other data modes
• Developing biologically inspired approaches for AI/ML
• Enabling measurement and instrumentation, including multiplexed and multispectral imaging and molecular profiling that resolve cellular features in tissues
• Experimental design and autonomous discovery to generate the…