Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Cranfield University
- Technical University of Munich
- Technical University of Denmark
- Nature Careers
- Curtin University
- DAAD
- Forschungszentrum Jülich
- Susquehanna International Group
- Leiden University
- Radboud University
- Delft University of Technology (TU Delft); Delft
- Lulea University of Technology
- CNRS
- Leibniz
- University of Nottingham
- University of Southern Denmark
- University of Utah
- ;
- ; The University of Manchester
- CWI
- Carnegie Mellon University
- Ghent University
- Inria, the French national research institute for the digital sciences
- Leiden University; Leiden
- Linköping University
- Monash University
- NTNU - Norwegian University of Science and Technology
- Radix Trading LLC
- SciLifeLab
- Temple University
- UNIVERSITY OF HELSINKI
- University of Amsterdam (UvA)
- University of Amsterdam (UvA); Amsterdam
- University of British Columbia
- University of Florida
- University of Groningen
- University of Sheffield
- University of Twente
- University of Twente (UT)
- Uppsala universitet
- VIB
- Vrije Universiteit Brussel
- ; Brunel University London
- ; Coventry University Group
- ; Cranfield University
- ; Newcastle University
- ; University of Exeter
- ; University of Surrey
- ; University of Warwick
- Abertay University
- Agricultural university - Plovdiv, Bulgaria
- Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung
- Chalmers University of Technology
- Copenhagen Business School , CBS
- Coventry University Group;
- Delft University of Technology (TU Delft)
- Duke University
- Eindhoven University of Technology (TU/e)
- Helmholtz-Zentrum München
- Imperial College London
- Instituto Superior de Economia e Gestão
- La Trobe University
- McGill University
- Medizinische Universitaet Wien
- Murdoch University
- National Renewable Energy Laboratory NREL
- National Research Council Canada
- Norwegian Meteorological Institute
- Queensland University of Technology
- Reykjavik University
- SINTEF
- Singapore Institute of Technology
- Swinburne University of Technology
- Technische Universität München
- UCT Prague
- UNIVERSIDAD POLITECNICA DE MADRID
- University of Adelaide
- University of California Irvine
- University of Cambridge
- University of Exeter
- University of Luxembourg
- University of Massachusetts Medical School
- University of Melbourne
- University of Newcastle
- University of Southern Queensland
- University of Texas at El Paso
- University of Twente (UT); Enschede
- University of Vienna
- University of Warwick
- Université Laval
- 80 more »
- « less
-
Field
-
, visiting researcher opportunities, access to modern GPU clusters for deep learning research, and strong academic-industry connections. CADIA's commitment to open science aligns perfectly with this project's
-
, Artificial Intelligence, Machine Learning or Cybersecurity or completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses in, for example, Machine Learning, Deep Learning
-
skills (Python preferred) and solid understanding of machine learning and deep learning, including computer vision techniques. Ability to read, write, and communicate scientific texts clearly; strong
-
substrates while advancing our understanding of deep learning through dynamical systems theory. You will work with two cutting-edge experimental systems: (1) light-controlled active particle ensembles
-
reducing demand. However, demand drivers are manifold, including technology advancements, population and economic trends, and their future developments come with deep uncertainties. Infrastructure policies
-
opportunity Experience in machine learning and deep learning Proficiency in one or more high-level programming languages such as Python, Java, or R. Strong commitment to interdisciplinary research and a track
-
), Deep Neural Networks. Probabilistic Machine Learning and Time-series Analysis. Industrial applications of AI (energy, process industry, automation). Software development experience in teams. Programming
-
robust and fast HSI-based detection method using deep learning (CNNs and pixel-wise classification). • Creating a comprehensive dataset of hyperspectral images for training and testing models
-
unique atmosphere where there is expertise to dig deep into computational modelling, while remaining connected to the experimental side. This interdisciplinary atmosphere has been a main catalyst for many
-
system using deep learning (DL). The project’s objectives include generating training data from synthetic datasets and real-world images (cadaver and actual intraoperative THR images), developing a marker