262 programming-"https:"-"FEMTO-ST"-"UCL" "https:" "https:" "https:" "https:" "https:" "CNRS" "Univ" positions in Switzerland
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- Paul Scherrer Institut Villigen
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- Swiss Federal Institute for Forest, Snow and Landscape Research WSL
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. Strong programming skills in Python and familiarity with a modern ML stack (e.g., PyTorch, hydra, zarr, dask) Experience in handling and processing large datasets or experience in high-performance
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novel research programme at the frontier of multimodal AI and ICU clinical cognition. Under the guidance of Prof. Jutzeler, a dedicated postdoctoral researcher, and collaborators, you will have access
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programming skills in Python Experience with machine learning systems or LLM-based architectures Experience working with complex data systems or developing applied AI prototypes Familiarity with modern AI tools
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in Mechanical or Aerospace Engineering, Physics, Computational Science, or a related discipline. The candidates should have: Solid programming skills (Python, MATLAB, or C++). Knowledge of the OpenCV
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- or nanoplastics, online or offline ice nucleation experiments. A good knowledge of programming languages such as Python, R, MATLAB or IGOR is expected. Excellent English skills, both in verbal and written
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Forest Studio is designed as a modular modelling environment that integrates ecological theory, empirical data, and modern scientific programming workflows. This position focuses on advancing
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Each doctoral student will lead a coherent subproject within this broader research program: Position 1: AI for Computational Thinking Focuses on designing and studying AI-assisted programming
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Strong background in seismic methods, signal processing, and/or wave physics Experience or strong interest in seismic data processing, imaging, and inversion Good programming skills (e.g., Python, MATLAB
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class research in the field of robotic fabrication in architecture and construction. The Chair of Timber Structures advances education and research in timber engineering through the Program for Excellence
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for the renewable energy sector via the M2A European Training Network (ETN), funded by the European Commission’s Horizon 2020 Marie Skłodowska-Curie programme. Project background M2A puts forward a robust methodology