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, a novel spatial discovery proteomics concept that integrates microscopic cell phenotyping with deep-learning based image analysis and global MS-based proteomics. This unique method was recently
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senior research position to work on projects related to computational analysis of mass spectrometric datasets. A major focus will be on the application of AI/machine learning models and other computational
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data analysis is required. The lab mostly uses R for data analyses; knowledge of R is not required, and the postdoctoral scholar will have the opportunity for mentorship and learning. To Apply: Motivated
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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
mechanism-driven AI and agentic AI frameworks (iGenSig-AI, G2K) that integrate biological knowledge with cutting-edge machine learning to transform omics data into actionable therapeutic insights
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The Section of Bioinformatics, DTU Health Tech is world leading within Immunoinformatics and Machine-Learning. Currently, we, together with a leading external pharma company party, are seeking a
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Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
close collaboration with a specific group (DARSA) specialized in developing and applying remote-sensing tools and innovative open-source machine-learning methods. Key responsibilities Develop effective
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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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, and machine learning. The environment at GBI will allow researchers to undertake ambitious, long-term, collaborative research, and we will actively support the translation of research to commercial
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from different fields: AI (machine learning, big database, etc) Semiconductors
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human health. Within this mission, the Iorio Group works at the intersection of computational biology, functional genomics, and precision oncology, integrating machine learning, large-scale CRISPR