Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
foundations of the understanding of animate and inanimate nature and the foundations of of natural science research that forms the paradigm of a particular historical epoch natural science. The history of each
-
/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
-
for three consecutive periods (2014-2018 and 2018-2022 and 2023-2026). ICN2 comprises 20 Research Groups, 7 Technical Development and Support Units and Facilities, and 2 Research Platforms, covering different
-
-impact weather A dedicated computer scientist/software engineer works alongside you to set up ICON-CLM on the HPC systems, support simulations and manage large data volumes—so you can focus on the science
-
7 Mar 2026 Job Information Organisation/Company CNRS Department Laboratoire d'informatique en image et systèmes d'information Research Field Physics Researcher Profile First Stage Researcher (R1
-
of space (mesosphere and lower thermosphere, MLT). The advertised position is primarily linked to the study of different metal atoms in the MLT and their relation to the re-entry of space debris. The project
-
Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 21 days ago
in particular computer vision. Particular topics of interest include visual comprehension, hyperspectral imaging, numerical and parallel optimization, and unsupervised learning. A particular emphasis
-
plasticity platform. Different machine learning strategies will be explored to capture the complex relationships between microstructural features and mechanical responses. In particular, the project will
-
. The team at AstraZeneca is based at our vibrant R&D site in Gothenburg, Sweden and applies biophysical technologies across a range of different modalities to support drug discovery from target validation
-
, data-driven surrogates) are widely used to obtain fast, approximate predictions. A major scientific challenge is therefore to combine information from models of different fidelity levels in a principled