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. Experience in coding (e.g., Python/R/Matlab) and experience in behavioural experimentation, statistics, or machine learning is desirable but full training will be provided. Interviews for this studentship
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-authored peer reviewed papers) • Well-developed statistical software skills (preferably in R, Python, GIS) • Competences in quantitative research methods – ideally knowledge of several of the following
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software as python packages Reporting findings and methods in conference and journal papers Your profile Masters, Diploma or equivalent degree in IT/computer science/statistics/applied mathematics/data
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) and immunoassays (ELISA); Bacterial culture and transformation; molecular cloning; Experience with immunocytochemical techniques and fluorescence microscopy; Programming in Python; Independence and
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in written and oral English. Programming skills with Python. Consideration will also be given to good collaborative skills, drive and independence, and how the applicant’s experience and skills
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experience in academic writing for publication (e.g. first or co-authored peer reviewed papers) Well-developed statistical software skills (preferably in R, Python, GIS) Competences in quantitative research
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is desirable. Basic understanding of embedded systems and processor architectures. Strong programming skills (C/C++, Python; hardware description languages such as HLS or VHDL are an advantage
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of LLMs is desirable. Basic understanding of embedded systems. Familiarity with fault detection, system reliability, or troubleshooting techniques. Strong programming skills (C/C++, Python; hardware
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or interest in AI/ML, data analytics, digital twins, or cyber-physical systems Good programming skills (e.g., Python, C++, or similar) Ability to work independently and as part of an international research team
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skills. Experience with programming, preferably Python and R, is required. Experience with mass spectrometry data, in particular metabolomics, and geometric machine learning is a plus. In addition to above