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Field
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degree (2.1 +/MSc) in mechanical / civil / environmental engineering, physics or a related quantitative field; strong computing skills (Python/MATLAB/GIS); interest in transport or urban futures. Position
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forms from land via inland waters to sea. You will synthesise, test, and use the reported methane kinetics from labs and culture experiments to construct the model using Python programming language. You
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experience with programming tools such as R or Python. An interest in acquiring new data science and programming skills is required. A keen interest in teaching and academic development. An excellent academic
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skills : We expect a candidate with a strong background in machine learning or statistics. The candidate must also be proficient in high-level languages like Python. Familiarity with single-cell date and
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Python or at least one other scientific programming language (e.g. FORTRAN, C, Matlab, R) good knowledge of English (written and oral) high degree of creativity and flexibility; ability to work under
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publication record #excellent programming skills in Python or at least one other scientific programming language (e.g. FORTRAN, C, Matlab, R) #good knowledge of English (written and oral) #high degree
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latitudes. Student profile: The project requires the student to be relatively proficient in Python programming to use and modify existing software, as well as for potentially developing new diagnostics and
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include: Developing the model using open-source Python software Planning and conducting experiments Analysing teardown reports and experimental data Validating and improving the model Publishing results in
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deep learning, generative models, or biomedical imaging/omics data Strong programming skills (Python, PyTorch, etc.) A commitment to interdisciplinary collaboration and impactful research How to Apply
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placement locations. Advanced metaheuristic optimisation algorithms, implemented in Python and interfaced with DIgSILENT PowerFactory, will be developed to determine the most effective PST locations and