Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- University of Oslo
- Nanyang Technological University
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- Harvard University
- INESC TEC
- Northeastern University
- University of Bergen
- University of Arkansas
- University of British Columbia
- Indiana University
- Loyola University
- Plymouth University
- UNIVERSITY OF NOTTINGHAM NINGBO CHINA
- Universidade do Minho
- University of New South Wales
- University of Nottingham
- Bowdoin College
- INESC ID
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- LINGNAN UNIVERSITY
- NTNU Norwegian University of Science and Technology
- UCL;
- UiT The Arctic University of Norway
- University of Beira Interior
- University of Colorado
- University of Kansas Medical Center
- University of Manchester
- University of Minho
- University of South Carolina
- University of Texas at Austin
- Aarhus University
- Brunel University
- CIC bioGUNE
- Carnegie Mellon University
- Cornell University
- Dalhousie University
- Dana-Farber Cancer Institute (DFCI)
- Durham University
- ETH Zürich
- European Space Agency
- FCiências.ID
- Francis Crick Institute
- Humboldt-Universität zu Berlin
- Johns Hopkins University
- King Abdullah University of Science and Technology
- Lawrence Berkeley National Laboratory
- Max-Planck-Institut für Bildungsforschung
- Montana State University
- NTNU - Norwegian University of Science and Technology
- OCAD University
- RMIT UNIVERSITY
- RMIT University
- SUNY University at Buffalo
- Simons Foundation/Flatiron Institute
- Tohoku University
- UCL EE
- UNIVERSITY OF SYDNEY
- Universidade de Coimbra
- University of Alabama, Tuscaloosa
- University of Denver
- University of Glasgow
- University of Idaho
- University of North Carolina at Chapel Hill
- University of North Carolina at Charlotte
- University of Rhode Island
- University of South-Eastern Norway
- Zintellect
- 57 more »
- « less
-
Field
-
) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic arms (e.g., UR5, Franka Emika
-
robots, robotic manipulators, or locomotion systems. Knowledge of machine learning, reinforcement learning, imitation learning, or computer vision techniques for robotics applications. Strong analytical
-
models to characterize lung cancer based on a non-invasive methodology. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - extend the knowledge of the state of the art in machine learning
-
in Computational Electromagnetics will be responsible for developing theoretical models and high-performance computer codes for modelling the interaction between electromagnetic waves and dielectric
-
. Advanced machine learning, reinforcement learning, and agent-based optimization techniques will be developed to reduce voltage deviations, cut active power curtailment, and improve system adaptability under
-
Ph.D. in neurobiology or a related field. 1 year post-doctoral experience Certificates/Credentials/Licenses Applicants must have a Ph.D. in neurobiology or a related field Computer Skills
-
focuses on developing cutting-edge statistical/machine learning methods for fitting complex network models to partially observed hospital infection data, leveraging patient movement data. This research will
-
for relevant systems of EF-hand proteins · Supervise and instruct graduate and undergraduate students in Dr. Pengfei Li’s lab Minimum Education and/or Work Experience A MD or PhD is required in a closely related
-
, including parsing and processing large document corpora. Strong understanding of machine learning or AI methods applied to health or biomedical data. Demonstrated ability to assess model outputs, identify
-
. - Criterion 2: Knowledge in the scientific areas of the project: Academic or applied knowledge in Software Engineering, Intelligent Systems/Machine Learning, and Interactive Technologies. - Criterion 3