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spoken English. Experience in Python and/or R. Experience with Linux and Bash. Experience with bulk, single-cell or single-nucleus RNA-sequencing data. Ability to troubleshoot and resolve
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or scripting for data engineering, for example using Python or PySpark. Software engineering practices such as Git, CI/CD and automated testing. API-based and other application integration patterns
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engineering, for example using Python or PySpark. Software engineering practices such as Git, CI/CD and automated testing. API-based and other application integration patterns. Semantic modelling and business
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programming skills in Python. Hands-on experience with LLMs (via APIs or open-weight models), for example prompt design, or tool/function calling. Working knowledge of common bioinformatic workflows (e.g
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-forming protein biology In vitro reconstitution and lipid bilayer systems Biosensor development or biology–electronics integration Basic programming skills (Python or similar) Familiarity with protein
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, and problem-solving skills Programming experience in Python or a similar scientific language Interest in machine learning and data-driven modelling Excellent written and spoken
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bioinformatics, computational biology, or related field. Strong experience with NGS data analysis and scripting (Python/R). Familiarity with immune repertoires, protein sequence analysis, and HPC
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Willingness to combine wet-lab experimental work and computational analysis Basic knowledge of statistics and/or programming (R, Python, or similar) Strong motivation to learn new techniques across
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Python and Git, with high standards for software quality, verification, documentation and user-friendliness strong problem-solving skills, you can take ownership of complex technical topics and figure out
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Willingness to combine wet-lab experimental work and computational analysis Basic knowledge of statistics and/or programming (R, Python, or similar) Strong motivation to learn new techniques across