-
for analysis utilizing quantitative data collected from prior trials of prevention programs and frameworks like PBIS, Double Check, Coping Power, MTSS-B, and the R-CITY model. Lead and co-author papers
-
cardiovascular treatments. In this role you will apply both machine learning predictive modelling and human genetic analyses of non-coding regulatory sequences to identify cell-specific targets for coronary artery
-
learning algorithms on graphs to model, characterize, predict, and design the thermal and physical behaviors of diverse material systems. Responsibilities also include the development of software codes
-
science of science, network science, and natural language processing. As part of a small research team, the postdoc will help lead efforts to provide a quantitative model of global competitiveness
-
cell biology. Additional preferred qualifications include: experience in mouse models of disease, flow cytometry, single-cell RNA sequencing analysis, and image processing and analysis. Qualified
-
of national research infrastructures Evaluating the evolution of Generative AI performance over time and across tasks Analyzing international AI models and their representations of the U.S. in global discourse
-
techniques for pulmonary diseases such as COPD, interstitial lung disease, asthma, pulmonary hypertension, and cystic fibrosis in human and preclinical modeling, including murine models. Postdoctoral
-
on the potential survival of consciousness after death. Today, our broad mission is the scientific investigation of phenomena that challenge currently accepted models of the nature of mind and consciousness
-
molecular mechanisms of carcinogenesis from a developmental perspective using Neurofibromatosis Type 1 (NF1), a common tumor predisposition human genetic disorder, as a model. While working on
-
and in vitro model systems to test novel biological mechanisms. Techniques of interest focus around high-dimensional immunophenotyping via mass cytometry and spectral flow cytometry, immunofluorescence