Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Nanyang Technological University
- National University of Singapore
- University of Oslo
- INESC TEC
- Zintellect
- Harvard University
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- University of Stavanger
- AUSTRALIAN NATIONAL UNIVERSITY (ANU)
- UiT The Arctic University of Norway
- University of Bergen
- University of North Carolina at Charlotte
- University of South-Eastern Norway
- Carnegie Mellon University
- Center for Drug Evaluation and Research (CDER)
- Cornell University
- DAAD
- Dana-Farber Cancer Institute (DFCI)
- INESC ID
- Macquarie University
- Max-Planck-Institut für Bildungsforschung
- Mayo Clinic
- Queen's University Belfast
- Ryerson University
- Tampere University
- The Francis Crick Institute;
- University of Agder
- University of British Columbia
- University of California
- University of London
- University of Maryland, Baltimore
- University of New South Wales
- University of Texas at Austin
- Virginia Tech
- 24 more »
- « less
-
Field
-
Council (ARC). You will develop a new model of electrical energy distribution, including renewable energy generation and electric vehicles. It will be used to optimize network resources across the grid
-
storage, demand‑side management, and hybrid PV–wind systems. Development of optimal strategies for increasing grid hosting capacity considering power quality, voltage stability, and losses. The research
-
-Informed Neural Networks (PINNs) and hybrid models that respect the physical laws governing the real-world system Applying Deep Reinforcement Learning (DRL) algorithms to optimize processes within simulation
-
the neural basis of flexible cognition. From basic functional circuit dissection in animals to human clinical studies, we ask how network disruptions cause cognitive deficits in neuropsychiatric disorders like
-
. Additionally, we intend to measure root water uptake using sap flow meters. The data will be integrated using recently developed physics-informed neural networks in order to translate apparent resistivity data
-
, subjective evaluation, and AIoT implementation. Key Responsibilities: Writing, debugging, optimizing and testing of source code with an emphasis on audio applications Maintenance and improvement of existing
-
research capacity through the application of optimization methods; - apply innovative techniques to problems related to smart energy networks. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING
-
, chromatin profiling, genomics, spatial transcriptomics and single-cell data. Apply statistical, machine learning, and network-based approaches to analyze high-dimensional biological data. Collaborate closely
-
mechanisms within potential CO2 storage complexes. Consequently, optimal deployment of CCUS solutions relies upon a robust understanding of fault network architecture in the subsurface. This project will
-
integrated with perfused microvascular networks. The role will focus on optimizing and standardizing vascularized organoid workflows, executing drug-response and combination-therapy assays, coordinating single