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Field
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proof-of-concept software tools Machine learning is a plus Strong analytical and programming skills are required (Python, Matlab, and C/C++). Prior proven experience in data-driven innovation projects is
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skills : We expect a candidate with a strong background in machine learning or statistics. The candidate must also be proficient in high-level languages like Python. Familiarity with single-cell date and
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and phylogenetic comparative analyses using R or Python Present research findings at scientific meetings and symposia Prepare and contribute to the publication of results in peer-reviewed journals Your
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latitudes. Student profile: The project requires the student to be relatively proficient in Python programming to use and modify existing software, as well as for potentially developing new diagnostics and
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and/or atom probe tomography Experience in image processing Experience in programming with Python or Matlab is strongly desired Team spirit as well as excellent communication and organizational skills
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external enrolment procedures. Selection criteria Demonstrated experience in programming and system development. Expertise in Python programming and data analysis. Experience developing Machine Learning
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an opportunity to network and get feedback on their work. Student profile: Some experience in data analysis would be very helpful, as would a working familiarity with a programming language (e.g. Python, R
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-oriented way of working A distinct advantage would be: Experience with data analysis and scientific programming (e.g. Origin, Igor Pro, Python, Matlab, Mathematica) A good command of written and spoken
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strain engineering is a plus. Data analysis skills (e.g. in Python, MATLAB or similar) are a plus. Excellent communication skills in English, both written and spoken Strong motivation to work
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statistical analysis and/or coding (e.g., R, Python, C++) Exposure to neurophysiological measurement methods (e.g., eye-tracking, pupillometry) Interest or training in technology law, digital regulation, or AI