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Field
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C, Python, MATLAB, or similar programming languages. Experience in scientific writing and academic publishing Students in the final year of their master's programme are also welcome to apply, provided
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their own initiative, good quantitative skills including programming (e.g. Matlab, R or Python) for the handling of data. Experience of neuroimaging (e.g. fMRI or MEG) and computational modelling is desirable
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results and communicate them clearly. Familiarity with R or similar analytical software, single-cell or low-input sequencing workflows, primary human tissue, Linux, Python or related bioinformatics tools
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biological field. You must possess documented experience managing spatial/scRNA transcriptomics workflows. Strong programming proficiency in R or Python is essential. Proven experience in data acquisition
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of these methods and are excited to expand their skillset with the approaches that they are not yet familiar with. Programming skills in python and R with experience in statistical data analysis, especially with
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have strong programming skills in at least one relevant language, such as R, Python, C or C++, and experience implementing, running, debugging or documenting computational models, simulations
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, Computer Science, Control, ML/AI, Mechatronics, or Applied Mathematics; strong analytical and writing skills; solid programming and mathematical skills (Python/C++, ROS valued); fluent English. Position-specific
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meetings. Proficiency in programming languages such as R and/or Python is required, alongside the ability to independently manage day-to-day research activities, take ownership of projects, and deliver high
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programming methods in Python or similar languages, and evidence of working well as part of a multidisciplinary research team. Informal enquiries may be addressed to Professor Tingting Zhu (email: tingting.zhu
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: computational linguistics / NLP cognitive science / psycholinguistics language acquisition or related fields Strong Python skills are essential, and experience working with corpus data or computational models