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, profilometry and AFM. You should also be familiar with theory of plasma discharges and have the background required to extract plasma parameters from plasma diagnostics data and with methods to perform time
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-resolved mass spectroscopy and should be versed in materials characterisation methods including XRD, nanoindentation, profilometry and AFM. You should also be familiar with theory of plasma discharges and
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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hours/week) funded until 31/03/2026 with a possibility of extension and is suitable for a Ph.D. student with relevant technical skills. Prior experience with medical imaging data and Python coding is
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contributing positively to a collaborative research environment. Desirable: experience with building energy or power system applications, cooperative or coalitional game theory, or high-performance computing
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systems theory Excellent analytical and problem-solving skills Desirable criteria Advanced programming and data analysis skills Computational neuroscience background Behavioral data analysis skills Strong
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technical skills. Prior experience with medical imaging data and Python coding is required. If you are interested about this position, please click 'apply now' and submit you application. In case you have any
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, econometrics or another relevant field. You will have substantial relevant research experience in complex trait genetics, a proven ability to code and a strong publication record. This is a full time, fixed term
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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High Performance Computing facility, where the current code is implemented. The candidate will, among other activities, extend the model to treat different management interventions, peat growth and decay