Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Delft University of Technology (TU Delft)
- Leiden University
- Eindhoven University of Technology (TU/e)
- Delft University of Technology (TU Delft); yesterday published
- Erasmus University Rotterdam
- Delft University of Technology (TU Delft); Published yesterday
- University of Amsterdam (UvA)
- Leiden University; today published
- Radboud University
- University of Amsterdam (UvA); Published today
- University of Twente
- Utrecht University
- Amsterdam UMC
- Amsterdam UMC; today published
- Delft University of Technology (TU Delft); today published
- Eindhoven University of Technology (TU/e); yesterday published
- Radboud University Medical Center (Radboudumc)
- Radix Trading LLC
- University Medical Centre Groningen (UMCG)
- University Medical Centre Groningen (UMCG); Groningen
- University of Amsterdam (UvA); yesterday published
- University of Twente (UT)
- Utrecht University; Published yesterday
- 13 more »
- « less
-
Field
-
? Join us to develop deep learning techniques for fusing acoustic sensor data with other vehicle sensors for robust multi-modal environment perception. Help shape the future of autonomous driving! Job
-
Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in challenging deep learning at its core? And
-
Are you interested in challenging deep learning at its core? And specifically, do you want to perform cutting-edge research and develop novel advances in hyperbolic deep learning for computer vision
-
(MRI) image quality using deep learning approaches. Quantitative MRI allows healthcare providers to quantitatively assess and characterize the state of a tumour and its microenvironment. This information
-
representations. In this project, you will substantially improve quantitative magnetic resonance imaging (MRI) image quality using deep learning approaches. Quantitative MRI allows healthcare providers
-
of Applied Math at the University of Twente has a diverse and vibrant environment for research in Machine Learning and adjoining areas, such as Deep Learning, Mathematical Statistics, Combinatorial
-
, mathematical logic or statistical learning theory. For PhD position 2, we appreciate prior experience in implementing deep learning models for graphs and networks. Our offer As a PhD candidate at UT, you will be
-
, we appreciate prior experience in implementing deep learning models for graphs and networks. Additional Information Benefits As a PhD candidate at UT, you will be appointed to a full-time position for
-
, and optimum sampling strategies. Proficiency in machine learning, deep learning, and artificial intelligence techniques. Familiarity with clinical applications and workflows. Basic understanding
-
with low-data, sparse, or noisy datasets, typical in early-stage drug discovery. Technical skills: Proficiency in Python (required). Practical experience with machine learning or deep learning workflows