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Field
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. Prerequisites Doctoral degree with quantitative training or research experience Training and experience in quasi-experimental methods is a plus Strong coding skills in R, Stata, or other statistical software
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genomic approaches Application of the modeling approaches in relevant downstream tasks Co-development of high-performance computing AI training codes for the first European Exascale Supercomputer JUPITER
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science, and applied plant research Example reading: Peleke, F. F., Zumkeller, S. M., Gültas, M., Schmitt, A., & Szymański, J. (2024). Deep learning the cis-regulatory code for gene expression in selected
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/distributed programming and/or solid UNIX skills Practical experience with ML/DL workflows and common software libraries Your experience should be documented in research papers and Open Source code projects
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expertise in at least one of the following areas: Synthetic biology Protein design Human T cell immunobiology T cell engineering Coding skills and familiarity with software such as Rosetta, ProteinMPNN
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using ATB’s online application form for the job advertisement, code 2025-DS-PD-15, at https://www.atb-potsdam.de/en/career/vacancies . Applications received after the application deadline cannot be
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quantitative training or research experience Training and experience in quasi-experimental methods is a plus Strong coding skills in R, Stata, or other statistical software package Good communication skills in
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strong publication history, including but not limited to conferences such as MICCAI, NeurIPS, ISBI, ICCV, ICML, ECCV, or others. Fluent familiarity with at least one coding language for ML or data analysis
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, methods, and algorithms into existing high-performance frameworks, the fast prototyping of new ideas in individual code, an interest in the entire simulation pipeline: starting from simple algorithms