Sort by
Refine Your Search
-
Listed
-
Employer
- Delft University of Technology (TU Delft)
- Eindhoven University of Technology (TU/e)
- Leiden University
- Utrecht University
- University of Twente (UT)
- European Space Agency
- Maastricht University (UM)
- University of Groningen
- University of Twente
- Wageningen University & Research
- Erasmus University Rotterdam
- Radboud University Medical Center (Radboudumc)
- SRON
- Universiteit Leiden
- University of Amsterdam (UvA)
- 5 more »
- « less
-
Field
-
design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale microbiome and genome
-
, you will design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale
-
Physics Informed Machine Learning method which exploits the advantages of physics-based and data-driven models, while mitigating the disadvantages. This research will contain experimental and modelling
-
structure modeling in cancer immunotherapy design. Profile A — AI PhD in machine learning, computer science, computational science, or a related field. Strong experience with deep learning (e.g., PyTorch
-
; experience with foundational AI model development/fine-tuning and machine learning and/or deep learning; strong programming skills (e.g., Python, JavaScript, PostgreSQL) with clear expertise in front-end and
-
: Within this international project, TU Delft will develop a machine learning-based forward operator to enable the assimilation of SAR imagery into the crop growth model. You will: Process SAR imagery over
-
missions. You will join a research team specializing in the detection and quantification of atmospheric emissions using satellite observations, atmospheric transport modeling, and machine learning. The team
-
cardiovascular care. Within the consortium, TU Delft contributes expertise in cardiac mechanics, soft tissue modeling, growth and remodeling, machine learning, and uncertainty-aware model personalization. As a
-
seeking a highly motivated Postdoctoral Researcher to join the Machine Learning cluster. The position is part of a research project investigating how visual foundation models can efficiently acquire new
-
shape an ambitious research programme at the intersection of computer vision, machine learning, and foundation models. Research Focus The project investigates how visual foundation models can efficiently