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Field
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. Strong programming skills in R and/or Python are essential, as well as prior experience in data analysis, statistics, or machine learning. The project involves large-scale single-cell and spatial
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Biology, or a related discipline Proficiency in Python and R, with experience in spatial transcriptomics data analysis and high-dimensional data visualization Demonstrated experience in building and
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optimization, AI, power electronics with good publication record· Good at coding in python with different DRL algorithms training, or good at digital platform development will be a preference Knowledge of
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bioinformatics for large-scale DNA sequencing (NGS) using Illumina, ONT, or PacBio platforms. Strong programming skills in Python Proficiency in Linux environments. Experience with container technologies (e.g
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AI/ML-based discovery Contribute to multi-modal data integration Your profile MSc or PhD in Computational Biology, Bioinformatics, Systems Biology, or a related field Strong programming skills (Python
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modelling (potentially Python), and qualitative research of socio-technical dimensions. You will collaborate with PhD candidates in chemistry, chemical engineering, and industrial economics. We are therefore
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field Strong programming skills (Python, R, Bash or similar) for omics data processing, QC, statistical testing, and visualization to support biological discovery Familiarity with working with Unix/Linux
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analyses Knowledge of modelling and programming in Python or MATLAB and CFD. Experience with 3-printing. Experience in building/assembling equipment on the lab/pilot scale. Previous experience of publishing
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disciplines, including biomathematics, biostatistics, and molecular biology. The candidate is expected to have a solid grounding in programming in R, Python, and mathematics/statistics.The main duties involved
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neural networks Documented experience with image analysis Strong competence in Python programming and Linux/HPC environments Prior experience of virtualization and software development Good social skills