Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- University of Oslo
- Harvard University
- Nanyang Technological University
- National University of Singapore
- UiT The Arctic University of Norway
- University of Stavanger
- Nature Careers
- Cranfield University
- AUSTRALIAN NATIONAL UNIVERSITY (ANU)
- Cornell University
- INESC ID
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- University of South-Eastern Norway
- CRANFIELD UNIVERSITY
- LINGNAN UNIVERSITY
- NTNU Norwegian University of Science and Technology
- The University of Alabama
- The University of Queensland
- Trinity College Dublin
- UNIVERSITY OF SURREY
- University of Texas at Austin
- ADELAIDE UNIVERSITY
- AbbVie
- Amgen
- Barnard College
- CSIRO
- Carnegie Mellon University
- Central Michigan University
- Cranfield University;
- Duke University
- Flinders University
- Johns Hopkins University
- King's College London
- King's College London;
- Northeastern University
- Oxford Brookes University;
- THE UNIVERSITY OF HONG KONG
- The University of British Columbia (UBC)
- UNIVERSITY OF SOUTHAMPTON
- University of Agder
- University of Bergen
- University of Birmingham
- University of Birmingham;
- University of California, San Francisco
- University of Denver
- University of Michigan
- University of Otago
- University of Tübingen
- Yeshiva University
- Łukasiewicz PORT
- 40 more »
- « less
-
Field
-
AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 1 month ago
machine learning, computer systems and software, and theoretical foundations of computing. We span traditional and modern thinking, connecting decades of computer science methodologies with modern data and
-
to machine learning algorithms in order to get uncertainty estimates for parameters governing the distribution of the observed data. The predictive Bayes scheme for uncertainty quantification contains a wide
-
solver who wants to be part of a dynamic team. Information about the Church Lab: Learn more about the innovative work led by Dr. George Church here: https://churchlab.hms.harvard.edu/ , https
-
The postdoctoral fellow will lead and co-lead projects that combine computational modeling, machine learning, and EEG to answer questions about scene understanding and neural representation. The fellow will work
-
in foundational neural models that learn from large unlabeled image datasets, also incorporating context from additional data such as wireline logs or well reports. You are suited for this position
-
exam before 15.06.2026. It is a condition of employment that the master's degree has been awarded. Background in optimization is required. Experience in machine learning is an advantage. Familiarity with
-
experience in at least three of the following: developing watershed model input datasets, geospatial analysis, applying large-scale hydrologic models, artificial intelligence and machine learning, computer
-
publication record. Outstanding data analytics, mathematical, and computer modelling skills. Excellent interpersonal communication and oral presentation skills in English Self-driven and strong team spirit Open
-
AI to predict safety outcomes for multiple targets and combination therapies Collaborate with research teams and data scientists to design data-driven strategies using machine learning/AI methods
-
sheet evolution, methane hydrate fluxes, or applying machine learning to geosciences to reconstruct glacial histories and project future ice sheet behavior. Please read this interview for more details