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area, with content covering robotics and machine learning, and excellent programming skills in Python. You should have research experience in either robotics or machine learning. You should also have
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in Environmental Modelling, Land Use modelling or another relevant field, with clear skills highly complementary to those of the JPP4JL research team Proven ability to write code in R or python
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well as proficiency in MATLAB, Python, or similar for real-time data analysis. Knowledge of implantable neural interfaces, electrophysiology, and stimulation technologies is also essential. Informal enquiries may be
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structured programming language, ideally python, and should have good experience in the analysis of photometric and spectroscopic data of exoplanet atmospheres. The projects will involve the use, modification
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; supervising student projects/practicals. · Demonstrable ability to model experiments using a range of software packages, such as Python, Matlab or Mathematica. · Experience of experimental
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Control engineering (experience with nonlinear systems is a plus) Machine learning and deep learning in context of physical systems Programming skills are required, with Python experience preferred. A good
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mechanical design Experience with beam shaping techniques (e.g., spatial light modulators, diffractive optical elements) Proficiency in programming languages like LabVIEW, Python, MATLAB, or C++, and
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CT or MRI scans. Your technical skills include Python programming and familiarity with deep learning frameworks (PyTorch or TensorFlow), along with a solid understanding of image processing techniques
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neurogenetics of Drosophila. This requires expertise in setting up and analysing behavioural tests, using appropriate analytical methods and software and skills in coding in e.g. R, MatLab, R, Python, as required
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with R or Python tools. Experience in processing of targeted and untargeted mass spectrometry datasets Demonstrated research competence and initiative through inernational publications in peer-reviewed