Sort by
Refine Your Search
-
Category
-
Country
-
Employer
- University of Oslo
- Harvard University
- Humboldt-Universität zu Berlin
- INESC ID
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial
- Northeastern University
- Łukasiewicz PORT
- Christian-Albrechts-Universitaet zu Kiel
- FCiências.ID
- Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S)
- King Abdullah University of Science and Technology
- NOVA.id.FCT- Associação para a Inovação de Desenvolvimento da FCT
- Nanyang Technological University
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- SUNY University at Buffalo
- Singapore University of Technology & Design
- The University of Memphis
- University of British Columbia
- University of Nottingham
- 9 more »
- « less
-
Field
-
are highly motivated, have interests in computer vision and neural networks, and want to both contribute to new advances in a field with real world applications. For more information and how to apply
-
research projects will be considered.) Technical expertise in machine learning and model fine-tuning – 10% Demonstrated experience with neural network training, loss function design, embedding-based models
-
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
-
Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | about 1 month ago
optimized design approaches. Run high-fidelity micromechanical simulations (finite element) to obtain the homogenised properties of a composite (ply-level) Use Convolutional Neural Networks (CNNs) to obtain
-
solutions to reduce power consumption in neural networks. In this project you will be involved in a collaborative effort investigating neuromorphic mixed-signal/near-analog circuits for next generation edge
-
mechanotherapeutics. 4. Neuromechanobiology: this area focuses on discovering the mechanobiological basis of neurons, neural networks, and the brain in memory, learning, cognitive function in health and
-
building automated systems or ML pipelines – 15% Demonstrated experience implementing structured, scalable, or automated software systems. Evidence of experience with neural networks, LLMs, or training
-
to design new learning frameworks and neural network architectures to advance our fundamental understanding of how the human brain forms perception and memories. In detail, you will use transformer
-
if you are highly motivated, have interests in computer vision and neural networks, and want to both contribute to new advances in a field with real world applications. Your research focus will be
-
applications of neural networks to the analysis of multi-omic data, models for predicting phenotypes using genotype data, biological data integration, etc.. Participation in these projects will include