tenure-track faculty position - Assistant Professor - Systems biology and Alzheimer's disease Make creative analytic contributions to understanding Alzheimer's disease at Rush University, a major center for aging and age-related disease research, located in Chicago, IL. Our Alzheimer's center offers an integrated clinical and research environment that has a constantly growing collection of multi-omic data on hundreds of individuals, at various ages and levels of cognitive function. These omics data can be studied against a backdrop of hundreds of cognitive and behavioral phenotypes that are tracked longitudinally in these subjects and thousands of others. We are searching for a tenure-track assistant professor to construct and contribute to systems biology models that enhance our understanding of age-related dysfunction and Alzheimer's disease. These models should lead to actionable, molecular or tissue-level predictions, which can be tested in experimental systems. Requirements: MD/PhD or PhD in Neuroscience, Mathematics, Systems Biology or related fields Experience with omics and big data, including any or ideally all of: structural and functional neuroimaging, proteomics, RNAseq, genetics and methylation Expertise in R, matlab and python Interests in network-based analysis of disease-related data Preference for: Publication history in graph theory, systems biology and neuroscience Familiarity with complex disease neurobiology Experience in design and implementation of large-scale cellular simulations Java, perl, parallel programming and batch execution systems Experience designing or performing high-throughput experiments Post-doctoral training The ideal candidate is free to develop an independent research program around aging, age-related disease and/or systems biology analysis. He/she will also collaborate with current faculty and develop and maintain external funding. Salary commensurate with relevant experience and skills. Instructions for applying: Send your CV to gaiteri@gmail.com AND ALSO include a paragraph on one of the following topics: 1. How will you identify key pathological mechanisms in a multi-omic disease setting, potentially in collaboration with experimental labs? 2. How will you develop multi-modal or multi-scale models of complex brain diseases? 3. What is the most under-appreciated statistical or mathematical technique that you expect could lead to insights in brain diseases?