Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- France
- Austria
- United Kingdom
- Germany
- Canada
- Singapore
- Norway
- Belgium
- Netherlands
- Sweden
- Australia
- United Arab Emirates
- Spain
- Luxembourg
- Denmark
- India
- Switzerland
- China
- Finland
- Czech
- Italy
- Morocco
- Poland
- Romania
- South Africa
- Barbados
- Europe
- Hong Kong
- Japan
- New Zealand
- Portugal
- Saudi Arabia
- Worldwide
- 24 more »
- « less
-
Program
-
Field
-
connection with the legal adoption of an eligible child, such as travel or court fees, for up to two adoptions in your household. To learn more, please visit: https://www.hr.upenn.edu/PennHR/benefits-pay
-
., deep learning methods, multimodal AI) for the automatic identification of behavioral cues during ecological interactions with people and the environment and analyses of video and speech/language data
-
the collective bargaining agreement. The salary range for this position is $84.78 - $109.85 (Hourly Rate). To learn more about the benefits of working at UCSF, including total compensation, please visit: https
-
Learning for Medical Imaging and Multi-Modal Data in Cancer Research Apply for this job See advertisement About the position Position as PhD Research Fellow in Deep Learning available at the Department
-
funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description A postdoctoral researcher
-
/unsupervised learning (regression, classification, clustering), ensemble methods, and deep learning architectures (CNNs, RNNs). Experience with explainable AI (e.g., SHAP, LIME) and radiomics preferred
-
, labs, and clinical events Apply deep learning and transformer-based approaches to longitudinal EHR data Integrate multi-modal data (EHR, labs, vitals, imaging, etc.) • Position 3: Postdoctoral Researcher
-
well as a Ph.D. in Art History or a very closely related field. Typically, they will be expected to teach 6-7 courses per year with the remaining 12.5%-25% of their workload dedicated to service. As
-
reconstruction and deep learning techniques. Participates in publications, scientific abstracts, and presentations as first author or co-author. Presents research results at scientific meetings. Participates in
-
Expertise: Familiarity with supervised/unsupervised learning (regression, classification, clustering), ensemble methods, and deep learning architectures (CNNs, RNNs). Experience with explainable AI (e.g