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, bioelectrical signalling and fibrotic cell-state transition. More information on the project can be found here: https://marcfernandezyague.com The successful candidate will conduct experiments at the Queen Mary
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Institute of Particle Astrophysics and Cosmology (BIPAC), on research aimed at extracting cosmological information from large-scale structure (LSS) and Cosmic Microwave Background (CMB) probes on very large
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-spectrometry-based proteomic and metabolomic workflows, quality control, quantitative data processing, statistical analysis and integration of proteomic and metabolomic datasets. You will also contribute to data
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treatment response. The role involves building robust and reproducible computational pipelines, interpreting complex biological data, collaborating across the CRC-STARS consortium, and contributing
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team working skills. Informal enquiries may be addressed to Prof Ioannis Havoutis (email: [email protected] For more information about working at the Department, see www.eng.ox.ac.uk/about
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clinical research, including performing clinical and neuropsychological assessments and collecting and processing biosamples, and will develop skills in database curation and analysis of clinical cohort data
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field. The successful candidate must possess expertise in distributed AI/ML systems and computer networks. Good knowledge and practical skills in distributed AI/ML, networked systems, data management, and
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laboratory skills involving vacuum physics, laser physics, quantum optics tools, lab software and data acquisition, cluster physics and mass spectrometry. Knowledge: experimental experience with macroscopic
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males and females respond differently to bacterial infection. The successful candidate will play a leading role in the imaging and data analysis components of the project. They will develop and apply high
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nitrogen-containing products and assess system performance. You will contribute to the design and testing of the prototype system, analyse experimental and simulation data, and publish the resulting research