Sort by
Refine Your Search
-
Category
-
Employer
- Delft University of Technology (TU Delft)
- University of Amsterdam (UvA)
- Utrecht University
- Wageningen University & Research
- Eindhoven University of Technology (TU/e)
- Leiden University
- Radboud University
- Amsterdam UMC
- HFML-FELIX
- Maastricht University (UM)
- Vrije Universiteit Amsterdam (VU)
- Radboud University Medical Center (Radboudumc)
- Royal Netherlands Academy of Arts and Sciences (KNAW)
- University of Twente (UT)
- ARCNL
- Centrum Wiskunde en Informatica (CWI)
- University Medical Centre Groningen (UMCG)
- Amsterdam UMC, location VUmc
- Erasmus MC (University Medical Center Rotterdam)
- KNAW
- Prinses Máxima Centrum
- Sanquin Blood Supply Foundation (Sanquin)
- Tilburg University
- University of Groningen
- 14 more »
- « less
-
Field
-
-on-brain.eu/ ). You will employ techniques such as organoid cultures (pluripotent stem cell and tissue-derived), transcriptomic analyses, CRISPR-Cas engineering, and other state-of-the-art tools. Your
-
to develop trusted data-sharing solutions for the heat transition. Within the project, you will have the opportunity to collaborate with other PhD candidates and a postdoctoral researcher working on
-
and a postdoctoral researcher working on complementary topics including decision-making under data-sharing constraints, privacy-preserving data sharing, governance and citizen participation. You will
-
University). The team will consist of Sander Nieuwenhuis, a postdoctoral researcher, the PhD student and a research assistant. If time permits, the PhD student will get the opportunity to contribute
-
interest in the human brain. Programming experience (Python, MATLAB) and proficiency in spoken and written English is required. Experience with or an interest in microscopy, quantitative image analysis
-
the Department of Rehabilitation Medicine, Amsterdam UMC. Thoroughout the project, you will collaborate with clinicians, clinical researchers, and a postdoctoral researcher based at the Department
-
) of fiber reinforced composites. Experience with the finite element method and programming experience in scripting languages like Python or Matlab. The position on structural health monitoring requires
-
, Computer Science, Electrical Engineering, Physics, (Applied) Mathematics, or a closely related field; a strong foundation in machine learning / deep learning and solid programming skills (e.g. Python, PyTorch
-
)medical or nutritional sciences.Preferably you have experience with working with large datasets in statistical software packages such as R, Python or SPSS. Proficiency in English at C1 level or higher (CEFR
-
machine learning research software, preferably using Python and PyTorch. An interest in foundation models, self supervised learning, multimodal learning, and 3D perception. An affinity for translating