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Targeted mutagenesis / protein engineering and screening of muteins Computational biology on an HPC cluster Teaching experience with bachelor and master students Candidates should have the following personal
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/articles/s42256-024-00821-x, https://pubs.acs.org/doi/10.1021/acs.analchem.5c06256, https://www.nature.com/articles/s41570-023-00570-2). The research is computational in nature but involves close
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application instructions), please visit: https://www.mpg.de/en/max-planck-postdoc-program . To submit your application online, please visit: https://postdocprogram.mpg.de The application deadline is April 13
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Cell Biology and Genetics, the Biotechnology Center, the Center for Systems Biology and the Center for Regenerative Therapies. Our institute also actively participates in the Cluster of Excellence
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Postdoc in experimental studies of phase behavior of ABC-miktoarm star block copolymers in thin f...
Experience with image analysis Experience with coding and high-performance computing, for example in python, C, etc. Experience with polymer and/or block copolymer physics and/or chemistry Experience with
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Temporary contract | 24 months | Belvaux Are you passionate about research? So are we! Come and join us The Luxembourg Institute of Science and Technology (LIST) is a leading Research and Technology
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peptide, mRNA, or gene therapy development related to mitochondrial disease. Computational biology / bioinformatics, especially ribosome profiling, disease gene discovery, or integrative multi‑omic
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and Circular economy, through the execution of five Strategic Research Programs: Data Science for Tires, Tire as a Sensor, End-of-Life Tire Valorization, Sustainable Materials for Next Generation of
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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
Postdoctoral Positions: Computational Genomics · AI-Driven Precision Oncology · Translational Cancer Biology Wang Laboratory, UPMC Hillman Cancer Center Department of Pathology and Human Genetics
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. The candidate will lead computational analyses of these datasets, using the laboratory’s suite of existing AI/ML tools to assign structures to unidentified peaks in metabolomic datasets (e.g., https