Sort by
Refine Your Search
-
Listed
-
Employer
- Delft University of Technology (TU Delft)
- European Space Agency
- University of Amsterdam (UvA)
- Tilburg University
- Eindhoven University of Technology (TU/e)
- Radboud University
- University Medical Center Utrecht (UMC Utrecht)
- University of Twente (UT)
- Delft University of Technology (TU Delft); yesterday published
- Erasmus MC (University Medical Center Rotterdam)
- Erasmus MC (University Medical Center Rotterdam); today published
- Leiden University
- Max Planck Institute for Psycholinguistics
- The Netherlands Cancer Institute
- University of Amsterdam (UvA); Published today
- Wageningen University & Research
- 6 more »
- « less
-
Field
-
qualification) in AI (e.g., machine learning, natural language processing or computer vision); A strong scientific track record, documented by publications at first-tier conferences and journals (e.g., NeurIPS
-
developers and many more, that are focused on bringing data, machine learning and statistical modeling into the products that we build for our clients or internal users. The data scientists in INGA furthermore
-
of you Required PhD in machine learning, physics, or a related field. Established expertise in deep learning (familiarity with graph neural networks, transformers, diffusion and flow based generative
-
subpopulations, as well as (plastic) cancer cell states that contribute to tumor progression, metastasis and therapy resistance. The candidate will lead several projects applying machine learning to (single-cell
-
, scientific machine learning (ML), wind energy, and advanced optimization? Join our team to develop cutting-edge solutions for aerodynamic design optimization of wind energy systems in complex urban
-
, Machine Learning, Computer Graphics/Animation, HCI, or a related field. Strong background in deep generative modelling (diffusion/transformers), multimodal representation learning, and experience in
-
Sciences at the University of Amsterdam (UvA). BDA works on the development of methodology for data mining, machine learning/deep learning, data fusion, and modelling and application of these methods
-
-analysis, regression modelling, machine learning). You have a solid basis in at least one common high-level programming language (e.g. R, Python). You enjoy collaborative research in international
-
-based and machine-learning approaches), the digital twin will provide decision-makers and industry stakeholders with actionable insights about when, where, and how corrosion risk evolves. As a postdoc
-
) with quantitative techniques (e.g., computer vision, physiological sensing, environmental monitoring, crowd behaviour analysis), as well as researching existing sources of knowledge in the literature and