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educational track record in condensed matter physics and optics, Familiarity with data analysis procedures and computer tools for automated data acquisition/processing (Python/C++/Labview programming languages
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/Computer Engineering, Computer Science, Applied Maths or related. Strong skills in AI techniques/ML/optimisation (Python/Matlab); familiarity with probabilistic modelling, time-series or control/power
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Matlab or Python Previous experience in Finite Element Analysis Application Procedure Informal enquiries are encouraged and should be addressed in the first instance to Professor Chris Martin (chris.martin
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of command-line tools, Python, or R for data analysis is a significant advantage. Language: Proficiency in English (written and spoken) is mandatory, as this is an international collaboration project. French
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programming skills (e.g., Python, MATLAB) would be desirable but are not essential. How to apply Interested applicants should contact Prof Tom Coulthard (T.Coulthard@mmu.ac.uk ) for an informal discussion
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analysis and at least one programming language (e.g. R, Python, MATLAB); Interest in sustainability and evidence-based environmental research. Desirable Knowledge of ecological survey design and biodiversity
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international research sites Your Profile: Completed master’s degree in mechanical engineering, material science, physics or a related field Experience in modelling and simulation (e.g. Python) Willingness and
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analysis software (e.g., Fiji/ImageJ). Advanced Cell Manipulation (e.g., genome editing via CRISPR/Cas9, flow cytometry/FACS). Basic familiarity with data analysis tools (e.g., GraphPad Prism, R, or Python
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developer and user meetings. You will acquire skills in programming (e.g. Python, FORTRAN, bash) development of quantum chemistry software and stand-alone tools a wide range of computational and quantum
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discipline. Experience with Python and relevant AI/ML libraries (e.g., PyTorch, TensorFlow). Demonstrated knowledge of multimodal data processing (e.g., vision, language, audio) and machine learning techniques