Sort by
Refine Your Search
-
Listed
-
Employer
- Delft University of Technology (TU Delft)
- University of Amsterdam (UvA)
- Wageningen University & Research
- Eindhoven University of Technology (TU/e)
- European Space Agency
- Leiden University
- University of Twente
- University of Twente (UT)
- AMOLF
- Erasmus MC (University Medical Center Rotterdam)
- Radboud University Medical Center (Radboudumc)
- Tilburg University
- Utrecht University
- 3 more »
- « less
-
Field
-
interest in learning, adaptation, and dynamical systems in physical contexts Experience with analytical and\or computational modeling. Proficiency in numerical methods and coding (Python, JAX, MATLAB
-
hydrodynamic coastal flow fields using SWAN, SWASH, SCHISM or a comparable model; writing python code to advect virtual macroplastic items in these flow fields using the Parcels-code.org framework; exploring
-
, Quantitative Genetics, Population or Statistical Genetics). Demonstrated experience in analytical and quantitative skills. Proficiency in programming and data analysis tools (e.g. Python, R, Fortran, Linux
-
, sediment transport/deposition, landscape change); You enjoy working with large datasets and applying statistical analysis and modelling approaches; You use scripting/programming in your research (e.g. Python
-
flows, or reinforcement learning-based design optimization. Strong programming skills in Python with experience in PyTorch, JAX, or equivalent deep learning frameworks. Ability to work independently
-
and digitizing archival data, strong knowledge of causal inference methods, good command of R and Python. Knowledge of machine learning methods is an asset. Strong command of English; command of either
-
-have: You can independently and confidently analyze quantitative data and you can write reproducible code (for example, in R or Python). Good-to-have: You have worked with large-scale text data, natural
-
Advanced proficiency in Python and C programming languages You should also have good interpersonal and communication skills and should be able to work in a multi-cultural environment, both independently and
-
languages, for example Python, and general purpose deep learning frameworks, such as Tensorflow or PyTorch; The interest and ability to share knowledge with other ESA organisational units. You should also
-
R or Python). Good-to-have: You have experience working with large-scale text or visual data, or datasets related to history or culture. You tackle complex data challenges with curiosity and are