Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- DAAD
- ;
- Forschungszentrum Jülich
- Technical University of Munich
- University of Southern Denmark
- CWI
- Cranfield University
- Curtin University
- KINGS COLLEGE LONDON
- Maastricht University (UM)
- Nature Careers
- Radboud University
- University of Cambridge
- University of Groningen
- Vrije Universiteit Brussel
- ; University of Bristol
- Helmholtz-Zentrum Geesthacht
- Inria, the French national research institute for the digital sciences
- Institut Pasteur
- La Trobe University
- Maastricht University (UM); Maastricht
- Monash University
- SINTEF
- Simula Metropolitan Center for Digital Engineering
- Technical University of Denmark
- Technische Universität Berlin •
- The Max Planck Institute for Neurobiology of Behavior – caesar •
- UCT Prague
- Umeå University
- Umeå universitet
- University of Adelaide
- University of Amsterdam (UvA)
- University of Amsterdam (UvA); Amsterdam
- University of Antwerp
- University of California Irvine
- University of Göttingen •
- University of Nottingham
- University of Nottingham;
- University of Twente
- University of Tübingen
- Université libre de Bruxelles (ULB)
- Vrije Universiteit Brussel (VUB)
- 32 more »
- « less
-
Field
-
to WAN and inter-domain networking, Excellent command of foundational and applied AI technology, from neural networks, distributed reinforcement learning to agentic AI and recent developments in
-
. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual annotations). The research will be conducted
-
engineering, computational neuroscience, artificial neural networks and bio-inspired robotics: "Rhythmic-reactive regulation for robotic locomotion" (Supervisor: Prof Fulvio Forni) will apply techniques from
-
. Strong coding skills for programming neural networks, machine learning and machine learning software frameworks (e.g. PyTorch or Jax) is a must. The ability for creative and analytical thinking across
-
applied to control problems or tiny RL scenarios. Explore digital hardware realizations of the proposed RL algorithms within existing spiking neural network chip designs. Quantitative comparisons with
-
feedback control, you will uncover fundamental connections between physical dynamics and neural network representations. We seek a highly motivated PhD candidate with an excellent master’s degree in physics
-
for brain signal acquisition Implementing an on-chip neuromorphic processor with a spike encoder and spiking neural network Developing a low-power spike-based transmitter. Setting up measurement systems and
-
inductive biases, we aim to identify key mechanisms that drive rapid learning in the visual system. The goal is to create a robust mechanistic neural network model of the visual system that not only mimics
-
directions will be pursued to enhance column generation using machine learning. The first line of research focuses on improving scalability by using Graph Neural Networks to identify and eliminate non
-
of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did