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data science. Fluency in English and Python are required. Fluence in Rust/C++, experience working with deep learning software stacks, extensive software development experience, and knowledge of machine
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companies. Writing well-documented, reproducible code and contributing to open-source Python packages released alongside the methods papers, including close collaboration with the second postdoc on a
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scientific ideas. Previous experience with statistical analysis, programming (e.g., R or Python), machine learning, or genomic data analysis is an advantage but is not required. We welcome applicants who
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surface dynamics Develop and apply Python-based image/data analysis tools for large time-resolved TEM datasets Establish quantitative links between 3D atomic structure, dynamics and catalytic functions of
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: Outstanding scientific programming skills (Python, PyTorch/JAX) with a proven track record of developing, debugging, and scaling complex RL pipelines or custom simulation environments. Clean public
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machine learning, preferably experience in probabilistic modeling, Bayesian machine learning, or graph representation learning. Programming skills (e.g., Python) and experience with deep learning
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applicants to: Have solid knowledge of econometrics and applied quantitative methods, with experience using Python, R, Stata, or similar tools, and be eager to learn and apply new methods. Be
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molecular immunology techniques. Bulk RNA-seq (and scRNA-seq) data analysis. Desirable: R/Python-based bioinformatics. As a person, you have good interpersonal skills, are inclusive and team-oriented
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, data analysis and programming skills (e.g., R, python, matlab, etc). We seek candidates that are team players who can play active scientific and social roles in a diverse and international lab setting
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to the research environment at the DDC Code proficiently in R or Python. Be able to communicate project results in English both in writing in academic contexts and to the broader public (Danish and