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, PorePy, or similar) Programming skills (Python, MATLAB, or similar) Experience with field data and/or underground experiments is an advantage Ability to work independently and lead research activities
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(e.g., in R or Python) A good standard of written and spoken English as well as a driver’s license are mandatory Good writing skills and the ability to work in a team with an interdisciplinary background
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, testing, APIs, data pipelines, containerisation, reproducible workflows (e.g. Docker, CI/CD, Nix), and programming in languages such as Python, Go, Rust, or similar. Exposure to data modelling or semantic
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, Earth Sciences, Physics, Engineering, or a related field Strong quantitative and analytical skills as well as programming skills (Python required; experience with seismic data processing is advantageous
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Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, or JAX Experience with HPC and GPU-accelerated computing Familiarity with foundation models / LLMs; interest in reproducible
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Required Experience: PhD in Geodesy, Geomatics, Aerospace Engineering, Signal Processing, or a related field Proven experience in GNSS data analysis and processing Very good programming skills (e.g., Python
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running data-driven or hybrid hydrological models Strong programming skills (ideally in Python and/or R) Experience in working with large datasets, ideally hydrological, meteorological or climate
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-learning models on distributed systems Strong programming skills in Python and familiarity with a modern ML stack (e.g., PyTorch, hydra, zarr, dask) and best practices MLOps Experience in handling and
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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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related field with experience in water chemistry, silviculture, experimental field work and large-scale data analysis. You must have good statistical skills and programming experience (e.g., in R or Python