Sort by
Refine Your Search
-
Country
-
Employer
- Nanyang Technological University
- University of Oslo
- Monash University
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- Harvard University
- Institut de Recherche pour le Développement (IRD)
- Johns Hopkins University
- King Abdullah University of Science and Technology
- MONASH UNIVERSITY
- Northeastern University
- SUNY University at Buffalo
- University College London (UCL)
- University of British Columbia
- University of Nottingham
- University of Texas at Austin
- Virginia Tech
- Zintellect
- 7 more »
- « less
-
Field
-
synergy with research carried out in our Center for Engineering Life (https://www.ucl.ac.uk/biosciences/cell-and-developmental-biology/centre-engineering-life-cl ), in particular synthetic developmental
-
on Artificial Neural Networks and Gaussian Process modelling, to accelerate processing optimisation. Consolidate experimental, techno‑economic, and sustainability data into robust technical evidence packages
-
-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
-
on research projects spanning evaluation of deep learning neural networks trained on signed language recognition. The fellow will work closely with the PI Annemarie Kocab and collaborator Alex Lu , Senior
-
of multi-agent coordination, decentralized control, target assignment, or swarm robotics. Familiarity with graph neural networks, attention mechanisms would be advantageous. Experience with computer vision
-
, neural networks) to be able to analyze data sources and bias mechanisms. Knowledge of the challenges of interoperability and digital infrastructure in resource-constrained countries. Knowledge of African
-
constructs, to study neural network dynamics, disease mechanisms, or drug response using in vitro or ex vivo systems. The candidate will collaborate with neuroscientists, stem cell researchers, and
-
; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic Qualifications Candidates
-
and computational fluid dynamics (CFD) Knowledge about physics-informed neural networks (PINNs) Language requirement: Good oral and written communication skills in English English requirements
-
Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https