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conducts research in the fields of biomedical and health informatics. In that work it focuses on the design, use, and evaluation of information systems that support and improve healthcare while protecting
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. The incumbent will be responsible for development of gene mapping populations, pathogen inoculation and phenotyping, marker design and genetic mapping, DNA cloning, and functional characterization of candidate
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using ImageJ. • Bioinformatics is highly desirable. • Statistical data analysis and data analysis. About the Department The Masonic Cancer Center (MCC) is an NCI-designated Comprehensive Cancer Center
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to light, flowering requirements/cues, and sod forming abilities along with leading general turfgrass management and seed production research. Position Responsibilities: 60% Research – Design, execute, and
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grant writing, research design, data collection, data analysis and dissemination. Candidates with interests in health equity, capacity building, knowledge translation, community-based participatory
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belonging in the group, in academia, and in our community. PI Santelli supports researchers interested in careers within and outside of academia. 50% RESEARCH - Help design and conduct laboratory experiments
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microbiological testing at interfaces within the compounding device prototype, assessment of the long-term stability of the fluid, and translating these findings into recommendations for the system design. Who
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strategies. Collaboration is a fundamental aspect of this position. You will work closely with a multidisciplinary team of researchers to design, execute, and analyze experiments. As a Postdoctoral Associate
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. Responsibilities (Must total 100%) 75% Experiment design and execution. 15% Data analysis and manuscript preparation. 10% Lab management and administration. Qualifications Required Qualifications: Doctoral degree
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computational modeling of behavior to identify the underlying circuit computations. Current projects in our laboratory emphasize task-structured behavior assays, including set shifting, reversal learning