Sort by
Refine Your Search
-
against independent sources. Data summary and analysis. Developing visualization tools to present results. Develop proposals that will be submitted to external funding agencies. Development of Research
-
Intelligence, npj Digital Medicine). Responsibilities Research Conduct independent and collaborative research in artificial intelligence with a focus on health and biomedical applications. Collaborate with PI
-
, staining, and mounting tissue specimens. Visualization and analysis of IHC imaging data Multiunit electrophysiological experiments, particularly using Neuropixels Software Expertise and Data Analysis: pCLAMP
-
. Experience with GPU-based training and high-performance computing. Interest in translating methodological contributions into high-impact medical AI venues (e.g., Nature Medicine, Nature Machine Intelligence
-
, urban and regional planning and visualization. As the only department of its kind in the nation, The Department of Landscape Architecture & Urban Planning (LAUP) at Texas A&M encompasses an unmatched set
-
, vehicle dynamics, and complex real-world environments. Research themes include model predictive control, reinforcement learning, reachability analysis, uncertainty quantification, and safety-critical visual
-
. Fundamental knowledge of phytoplankton ecology Intermediate skills in Microsoft Office Suite (Word, Excel, PowerPoint, and Outlook). Excellent verbal and written communication skills, including experience
-
Institutional Review Board (IRB) submissions at a major academic institution. Experience utilizing data visualization software (e.g., Tableau, Power BI, R Shiny) to design and implement interactive dashboards
-
. Knowledge of characteristics associated with mild cognitive impairment capturable with digital interfaces. Experience with agentic large language models Experience in artificial intelligence to develop
-
quantitative models. Interprets the data and explain the results in research discussion. Implements, applies, and evaluates tools to process, integrate, and visualize high-throughput sequencing datasets Data