Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- Germany
- United Kingdom
- France
- Spain
- Netherlands
- Sweden
- United Arab Emirates
- Portugal
- Austria
- Belgium
- Hong Kong
- Norway
- Denmark
- Italy
- Poland
- Singapore
- Finland
- Switzerland
- Canada
- Romania
- Greece
- Australia
- Brazil
- Croatia
- Ireland
- Japan
- Luxembourg
- Morocco
- Saudi Arabia
- Estonia
- Lithuania
- Vietnam
- 23 more »
- « less
-
Program
-
Field
-
cells Key methods will include: Gaussian Processes (heteroscedastic & multivariate) Operator-valued and deep kernels Active Bayesian experimental design Physics-informed neural networks Closed-loop
-
scenarios Convolutional neural networks for computer vision require substantial computing resources and introduce significant latencies even in modern GPU systems. This project investigates neuromorphic
-
) modules into safety-critical embedded systems (autonomous vehicles, drones, industrial and medical devices) raises major safety and security concerns. These modules, often based on deep neural networks
-
. “Building a digital twin for network optimization using graph neural networks”. In: Computer Networks, 217 (2022), p. 109329. Funding category: Contrat doctoral Concours sur dossier PHD Country: France Where
-
conducting experiments for training and evaluating deep neural networks Knowledge of multi-modal learning, transfer learning, transformers, or self-supervised learning Experience in dealing with large medical
-
incorporating context from additional data such as wireline logs or well reports. You are suited for this position if you are highly motivated, have interests in computer vision and neural networks, and want
-
of multi‑radio systems, interference management, and energy‑efficient network design. Familiarity with machine learning applications in communications, including neural networks and federated learning
-
Los Alamos has been rated #3 in the Best Counties to Live in the USA. Apply Now https://lanl.jobs/search/jobdetails/sparse-neural-network-design-post-doctoral-research-associates/e80bfca8-271e-4be8-b205
-
, and similar equipment. Proficiency in Python programming including ability to install and use spiking neural network simulators such as SNNTorch, NEST, Nengo, etc. Experience with semiconductor memory
-
the design and analysis of such models. PhD position 1 will focus on developing new graph-theoretic frameworks for analyzing graph learning models, such as Graph Neural Networks or Graph Transformers. PhD