-
, United States of America [map ] Subject Areas: Computer Science Machine Learning Mathematics / applied mathmetics , Mathematical Sciences , Partial Differential Equations , Statistics Appl Deadline: none (posted 2025/08
-
, United States of America [map ] Appl Deadline: (posted 2025/09/04 05:00 AM, listed until 2026/02/21 04:59 AM) Position Description: Apply Position Description Postdoctoral Associate – Scientific Machine Learning
-
regional leadership in biostatistics, genomics, biomedical informatics, artificial intelligence and health data science. The Postdoctoral Associate will conduct research in statistical machine learning and
-
/ ) research examines learning and conceptual change in young children with a focus on social learning and social cognition. Research topics include: mechanisms of causal learning, the developmental origins
-
the global health scenario and domestically for dissemination, and plenty of opportunities for career advancement. •Learn background/research methods of studies for which analysis is conducted with limited
-
learning environments that are free from harassment and prohibited discrimination. Duke prohibits discrimination and harassment in the administration of both its employment and educational policies. Duke
-
(e.g. ecological theory and mathematical modeling, hierarchical statistical modeling, machine learning, remote sensing, geospatial statistics) • Demonstrated ability to conduct independent research and
-
Carolina 27705, United States of America [map ] Subject Areas: Art Education Teaching and Learning History / History , Oral History History of Art / History of Art Social Movements Appl Deadline: 2026/06
-
for genomics (e.g., generative models, transformers, agentic workflows) and/or statistical learning (e.g., network & spatiotemporal modeling, functional/longitudinal data, time-series). Analyze single-cell
-
data, identifying structural errors in the dataset, and for maintaining a record of all steps from data extraction to dataset assembly · Fitting of machine learning models · Development of instrumental