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to disentangle co-occurring conditions using multi-omics data in Down syndrome. The position will be jointly mentored by Dr. Casey Greene in the Department of Biomedical Informatics and is funded by the NIH
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) structure/function and defining how CAR structure impacts brain tumor response and resistance. The postdoctoral fellow will conduct experiments and assist with project planning and data interpretation, as
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will help refine and validate computational models using large-scale, multi-cohort EEG data, applying advanced signal processing, deep learning, and bioinformatics approaches to: Brain aging phenotypes
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interdisciplinary and highly collaborative research in ovarian cancer to predict clinically relevant tumor features. The researcher will work on large and diverse molecular datasets, primarily consisting of bulk and
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heavily on infectious disease mechanisms, with active projects centered on bacterial proteases that manipulate host pathways and large macromolecular assemblies involved in microbial metabolism
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populations and synaptic inputs Large-scale extracellular electrophysiology (Neuropixels) Biophysical modeling and construction of ML-based models Statistical analysis of single-cell and population-level
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experiments, analyze data, and interpret and publish results. It is the Fellow’s responsibility to fully understand the technical details of Dr. Collins’ projects as well as how each applies within the larger
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diagnosed and treated for pancreatic cancer Collect clinical data from patients diagnosed with pancreatic cancer, including both resected and non-resected cases, across all stages of treatment, in
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outcomes. Preliminary data shows that administration of 5 candidate placental proteins in prematurely delivered guinea-pig pups had a remarkable positive effect, including reducing mortality from 33% in
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to the research project Record results of experiments: tabulate research data and maintain a detailed, easily understandable laboratory notebook Interpret experimental results Collaborate with PI and others in