Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- ;
- Technical University of Munich
- DAAD
- Nature Careers
- Chalmers University of Technology
- SciLifeLab
- University of Groningen
- ; University of Bristol
- CWI
- Cranfield University
- Curtin University
- Helmholtz-Zentrum Geesthacht
- Technical University of Denmark
- Vrije Universiteit Brussel
- ; Max Planck Institute for Psycholinguistics
- ; The University of Edinburgh
- ; University of Essex
- Aalborg University
- Duke University
- Fraunhofer-Gesellschaft
- Ghent University
- Institut Pasteur
- Monash University
- NTNU - Norwegian University of Science and Technology
- Radboud University
- The Max Planck Institute for Neurobiology of Behavior – caesar •
- Umeå University
- Universiteit van Amsterdam
- University of Adelaide
- University of California Irvine
- University of Göttingen •
- University of Minnesota
- University of Nottingham
- University of Twente
- University of Tübingen
- 25 more »
- « less
-
Field
-
architectures and principles from Bayesian neural networks and biological sequence models, including large DNA and protein language models. The project also aims to develop a prototype federated learning
-
programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning
-
skills in one or more languages (Python, C/C++, or others) experience in mechanical testing profound knowledge of machine learning methods (e.g., neural networks, Gaussian processes, active learning
-
to be developed. One promising research direction is the use of physics-informed deep learning, such as physics-informed neural networks or deep neural operator networks. Tasks: Work in a team on national
-
. The School comprises of four Research Groups, which are: Artificial Intelligence Brain Computer Interfaces and Neural Engineering Communications and Networks Robotics and Embedded Systems Research within
-
. This project will rely on recent advances in neural networks to develop machine learning potentials (MLPs) for MD simulations of realistic nanomaterial/coolant-liquids and use these to gain fundamental insights
-
learning approaches and develop a theoretical understanding potentially based on differential geometry. In particular, deep neural networks perform surprisingly well on unseen data, a phenomenon known as
-
their expertise together to establish neural organoid models recapitulating aspects of neural-microglia interactions in neurodegenerative diseases at Ghent University. About project MINDFUL: Lipid accumulation in
-
Appropriate computational skills and knowledge of programming languages (Python, C++, etc.) Experience with Machine and Deep Learning models and software (Keras, Scikit-Learn, Convolutional Neural Networks, etc
-
on agentic approaches, where an LLM interacts with visual tools, which may themselves be neural networks. Central challenges include enabling LLMs to reason about visual structures, designing