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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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study to be used as parameters in the model. 4) Model Implementation: Implement the model in Python, perform testing, and validate its performance based on the collected data. Applications must be
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experience in pytorch and/or tensorflow) knowledge of statistical methods and programming (R and Python) prior experience in machine learning and ideally also on applications in the health domain and/or
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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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Experience and practical knowledge of programming languages and tools (e.g. Python, Java, etc.) Knowledge in Software Engineering, AI, machine learning is an advantage Experience with software observability
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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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. Knowledge of Python and analysis of sequencing data is a plus. Fluent oral and written communication skills in English. We are looking for a highly motivated candidate who is interested in identifying and
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and interest in working with custom-built or modified instruments Solid programming skills (e.g. Python) are desirable Ability to work independently and self-reliantly, while maintaining strong teamwork