65 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Duke University
Sort by
Refine Your Search
-
Computer Science and Electrical and Computer Engineering departments, and the School of Medicine. The group emphasizes collaborative and multidisciplinary work and brings together expertise from machine learning
-
, Duke University Biology Department to study how archaeal microbial communities respond to stress in hypersaline environments. A PhD in computational and/or experimental biology is required in fields
-
with various methods that can incorporate domain-based constraints and other types of domain knowledge into machine learning and applying these techniques to problems in computational creativity
-
. The Postdoctoral Associate will apply his/her technical skills toward development and implementation of machine learning, computer vision, and other algorithms for analysis of medical images and prognostication as
-
team to unravel the mysteries of membrane ion and lipid transport and their roles in various diseases. Minimum Requirements: PhD in biochemistry, biophysics or cell biology Preferred Qualifications
-
the remotely sensed LST to compute the spatial statistics, run the HydroBlocks model over the Contiguous United States, and evaluate model deficiencies and model improvements to improve the modeling of spatial
-
Appointee holds a PhD or equivalent doctorate (e.g. ScD, MD, DVM). Candidates with non-US degrees may be required to provide proof of degree equivalency. 1. A candidate may also be appointed to a postdoctoral
-
, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control
-
Preferred Qualifications: A PhD or MD/PhD (or equivalent) in biological sciences (cell & developmental biology or a related field) which was awarded not more than 18 months ago. Evidence of successful
-
computational and data analytical methodology development and implementation; experience in supervised and unsupervised machine learning, low-dimensional models or deep learning models, and willingness to learn