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, supported by Innovate UK. The core task will be developing, expanding, and automating sophisticated simulation models of complex dairy farm systems using Python. This role focuses on translating real-time
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relevant discipline in engineering, science, or mathematics. Experience with modelling, simulation, optimisation, or programming (e.g. Python, MATLAB, C++, or similar) would be advantageous, though not
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methodologies. Applicants must demonstrate strong programming skills in at least one scripting or programming language (e.g. Python, R, Perl, Nextflow, C++, or Java) and experience in areas such as database
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We are looking for a researcher, whose expertise lies in Discontinuous Galerkin discretisations, compressible flows, Fortran, Python, to work with Luis Espath & Prof Paul Houston jointly with Space
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-throughput experimentation is desirable. Proficiency in programming languages (Python/MATLAB) commonly used in machine learning applications is desirable but learning can be completed during the PhD. Excellent
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, mathematics, or related scientific disciplines. Skills in numerical tools and programming are desirable (MATLAB, python, C++ etc). Any experience or capabilities in engineering design or manufacturing methods
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data analysis on Matlab. • Developing and using models to characterise the soft robots (both sensor and actuator). • Knowledge of programming (C/C++/python/MATLAB), using a prototyping board like Arduino
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. Desirable Skills (an advantage, not a requirement) Data analysis skills in python. An interest in energy policy / the economics of energy. Numerical modelling. Eligibility This studentship is available for UK
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/teaching experience. The confidence to deal with uncertainty and tackle any problem without a defined final answer. Desirable Skills (an advantage, not a requirement) Data analysis skills in python. Granular
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proficiency in Python (e.g., NumPy, Pandas, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with supervised