60 phd-in-computer-vision-and-machine-learning Postdoctoral positions at Duke University in United States
Sort by
Refine Your Search
-
, United States of America [map ] Subject Areas: Mathematics / applied mathmetics , Mathematical Sciences , Partial Differential Equations , Statistics Machine Learning Computer Science Appl Deadline: none (posted 2025/08
-
Duke University, Electrical and Computer Engineering Position ID: Duke -Electrical and Computer Engineering -POSTDOCYIRANCHEN [#30336] Position Title: Position Type: Postdoctoral Position Location
-
, 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
-
quantitative methods and excited about discovering physical principles of biological organization. Minimum Requirements: PhD in a scientific disciplines, ideally Biology, Bioengineering, Physics or Math
-
, United States of America [map ] Subject Areas: Chemistry / Bioinformatics , Chemical biology , Computational Appl Deadline: 2025/09/15 11:59PM ** Position Description: Apply Position Description Job Opening: Postdoctoral
-
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
-
, and Alzheimer's research. Qualifications: · Ph.D. in Computer Science, Biostatistics, Bioinformatics, Biomedical Engineering, or a related field · Expertise in deep learning and its
-
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
-
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