Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- European Space Agency
- University of Groningen
- University of Twente
- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); Delft
- Eindhoven University of Technology (TU/e)
- Leiden University
- University of Twente (UT)
- University of Twente (UT); Enschede
- Wageningen University and Research Center
- Eindhoven University of Technology (TU/e); Eindhoven
- Leiden University; Leiden
- University of Amsterdam (UvA); Amsterdam
- Wageningen University & Research
- CWI
- Erasmus University Rotterdam
- Maastricht University (UM)
- Maastricht University (UM); Maastricht
- Radboud University
- Radboud University Medical Center (Radboudumc); Nijmegen
- The Netherlands Cancer Institute
- The Netherlands Cancer Institute; Amsterdam
- University of Amsterdam (UvA)
- 13 more »
- « less
-
Field
-
, the researcher will develop theory and algorithms for (hybrid) model selection that allows to exploit domain knowledge through interactive learning. For this we will build on the minimum description length (MDL
-
trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with these challenges. By
-
well as to optimize the tooling geometry. These process simulations require efficient numerical algorithms to be practical and to enable robust optimization. Therefore, in this project you will: Develop efficient
-
-learning energy trading algorithms that are able to cope with these challenges. By leveraging real-time data, developed algorithms continuously adapt to market dynamics and respond to changing market signals
-
computational methods for the analysis and integration of –omics data. The group has a strong track record in (integrative) computational omics analysis, algorithm development, machine learning and scientific
-
, heavily relying on clinician expertise. This project funded by the Hanarth fund combines ultrasound imaging with histopathology data to train advanced AI models for automatic tumor segmentation, enabling
-
deep learning algorithms. We welcome applications from individuals with experience in: Experience developing deep learning models for real-time image/video segmentation, object tracking, reinforcement
-
Development of machine learning (including deep learning) algorithms to predict links between gene clusters and metabolites, and to predict antimicrobial activities associated with these Collaboration with
-
within a cross-functional team, including software developers, electrical and mechanical engineers. Experience and strong understanding of machine learning algorithms, mathematical modelling, and
-
interpretation is subjective, heavily relying on clinician expertise. This project funded by the Hanarth fund combines ultrasound imaging with histopathology data to train advanced AI models for automatic tumor