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and gene regulation with a strong interest in driving development of sequencing methods to identify mRNA decay substrates and decipher degradation mechanisms. The role The successful candidate will
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generation and data analysis. About the lab The focus of Lehner lab is using massively parallel selection experiments to tackle the fundamental encoding problems of molecular biology. Towards this
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train, benchmark, and develop deep learning architectures. They will work in high-performance computing environments and apply expertise in statistical mechanics, molecular dynamics, and physics. The
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high-performance computing (HPC) environments Understanding of reproducibility and good practices in scientific computing Basic experience with container technologies such as Docker or Singularity
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computing (HPC) environments Understanding of reproducibility and good practices in scientific computing Basic experience with container technologies such as Docker or Singularity/Apptainer Experience
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) using large-scale empirical data You have experience setting up Nextflow pipelines You have experience working in high-performance computing environments You have programming competence in R and/or
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large-scale empirical data You have experience setting up Nextflow pipelines You have experience working in high-performance computing environments You have programming competence in R and/or Python
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biological datasets • Experience working in high-performance computing environments • Experience running, adapting, maintaining and troubleshooting computational pipelines • Experience with Nextflow or a
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partners and regulators. The successful candidate will document code and methods so that analyses can be reviewed, reused and extended within the group. Key responsibilities: Build and maintain documented
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that combines state-of-the-art super-resolution microscopy with genomic approaches. AI-driven and computational methods are applied to analyze super-resolution imaging data to elucidate, identify, and