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Field
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xenotransplantation by analyzing the genetic makeup of pig menisci. Using single-cell and long/short-read sequencing, we will build a detailed reference transcriptome, compare it with human data, and explore zonal
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bioinformatics, deep learning and/or biomedical image and clinical data analysis (e.g. Linux, R programming) is essential. The appointees will need to perform data analysis of single cell RNA-sequencing
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immunogenicity with DTU’s strengths in immunoinformatics and machine learning, to address this challenge. Our work will provide novel insights into the current T-cell activation assays and datasets, and enable
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researcher to join the newly established Photosymbiosis Focus Group at the Helmholtz Institute for Functional Marine Biodiversity (HIFMB). You will combine state-of-the-art OMICS approaches, cell biology, and
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interact with a set of challenging scenarios. This project is funded by the European Space Agency (ESA) and is in collaboration with Autodiscovery. TMNT will focus on a team of highly mobile Unitree A2
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Postdoctoral Research Associate - Hybrid Computational-Experimental Scientist in Bacterial Drug Resp
to antibiotics and host-like conditions. • Develop and apply statistical or machine-learning methods for interpreting single-cell and genomic datasets. • Work closely with wet-lab scientists to design perturbation
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/i/a screening, single-cell multiomics, tissue engineering, and animal models. Our current research primarily focuses on four key areas: 1) Developing robust, chemically defined differentiation
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are seeking motivated candidate who has experience in the following aspects: Biology of C. elegans; cell culture particularly induced pluripotent stem cells and neurons; recombinant protein production
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, an interest in mapping the specialized translation landscape in hosts, and a willingness to learn, teach, and troubleshoot techniques in a team environment. Responsibilities 60%- Virology assays and cell
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We are seeking a highly motivated Postdoctoral Researcher to join the FNR AI-HPC 2025 BRIDGES project GenePPS, which investigates how machine learning can enable prediction of gene perturbation