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biochemical models, data assimilation, spatial analysis and GIS approaches. • Programing skills (e.g. R or Python) for data manipulation and visualisation, and to perform statistical analysis (e.g. mixed models
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skills (e.g. R or Python) for data manipulation and visualisation, and to perform statistical analysis (e.g. mixed models). Knowledge of any of the following will be an advantage: geo-statistics, AI
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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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Hands-on experience with 3D reconstruction, multi-view geometry, or neural rendering Experience with appearance reconstruction, reflectance estimation, or material/BRDF modeling Proficiency in Python and
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datasets (petabyte-scale), data management and data quality control Very good skills with programming languages such as Python/R/Julia, use of scientific data libraries (e.g., xarray, netCDF4, HDF5, Zarr
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analytics / atmospheric monitoring of greenhouse gases and interest in automated setups and field work. Knowledge of scientific data analysis (e.g. Python, MATLAB or R). Good verbal and written communication
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Required skills Master’s degree in STEM Strong proficiency in Python Practical experience with PyTorch Solid understanding of unsupervised machine learning and deep learning Excellent communication skills
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Vision, or a related field. Proven experience in photogrammetry, image-based 3D reconstruction, and cultural heritage digitization. Solid programming skills (e.g., Python, C++) and familiarity with
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, program synthesis, semantic parsing, tool-augmented/agentic workflows) 2. Symbolic methods (logic/constraints, SAT/SMT, theorem proving, planning) * Strong software engineering skills (typically Python
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: Experience with analyzing GPS tracks Good data-handling skills and ability to use R (compulsary) and preferably also Python and/or GIS competently Statistical/causal inference knowledge PhD degree in a related