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hyperspectral imaging is necessary. Proficiency in Python programming, particularly with Python libraries such as Spectral Python (spectral), NumPy, SciPy, and OpenCV and familiarity with at least one engineering
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ultrasound sensing and hyperspectral imaging is necessary. Proficiency in Python programming, particularly with Python libraries such as Spectral Python (spectral), NumPy, SciPy, and OpenCV and familiarity
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notebooks, Python or similar, are merits. About the employment The employment is a temporary position of 2 years according to central collective agreement, with the possibility of an extension of up to 1 year
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in computational fluid dynamics Experience in computer programming, in particular Python and Matlab, and in CAD and CAE tools Ability to work independently and also to enjoy collaborating with others
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, optimization, or related fields. Proficiency in scientific programming (e.g., Python, Julia, or C++) and experience implementing optimization algorithms. Proven ability to work both independently and in
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field. Demonstrated experience with satellite remote sensing for large-scale vegetation or forest monitoring, including Landsat and/or Sentinel-1/2 time series. Strong proficiency in Python for geospatial
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. Documented research experience in modern deep learning (e.g. generative models, Bayesian deep learning or large pre-trained models) and excellent programming skills in Python and a modern deep learning
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models and/or probabilistic modelling, and excellent programming skills in Python and a modern deep learning framework (e.g., PyTorch or JAX) are required. Excellent skills in spoken and written English
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of the following areas will be beneficial. Experience with machine learning or data-driven modelling, particularly for chemical or process systems Experience with Python, or similar tools for data analysis and
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language (C++, Python, Rust, …) One high-quality first-author paper (journal or top-tier conference) You are expected to be somewhat accustomed to teaching, and to demonstrate good potential within research