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sequencing etc.) Expertise in at least one lineage of land plants (i.e., a taxonomic focus) Experience with at least one coding language or a strong background in statistics Experience with high-performance
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neuroscience or developmental biology or equivalent Excellent skills in mRNA techniques, especially HCR labelling Confocal microscopy skills Computational experience, including coding Experience in using
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across the study team including: overseeing data transfer agreements between institutions; version-controlling and archiving code and leading data cleaning, harmonisation and analysis. Throughout
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relevant PhD or equivalent qualification/experience in a related field of study, and will have experience in the coding and development of RShiny apps. The successful applicant will be a nationally
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and deploy advanced deep learning and foundation models for surgical scene understanding segmentation, tracking, and operator assistance. You will write, test, and optimise Python and C++ code for real
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: Comprehensive personalised Assessment, early Risk Evaluation and clinical management” (HER-CARE) project. The successful applicant will work on the project “Assessing the role of rare germline non-coding genetic
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About Us The School of Biomedical Engineering & Imaging Sciences has attracted multi-million pound research funding and collaborates with other universities and Faculties within KCL, the NHS, charitable trusts and foundations, international Universities and industry. The post holder will work as...
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discipline *Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant. Expertise in atmospheric physics and chemistry Enthusiasm for coding of complex atmospheric
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learning in chemistry would be advantageous, as would familiarity with ML approaches for atomistic modelling (e.g., MACE, ACE, NequIP, PhysNet, reactive MD). Prior contributions to scientific code
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of code to conduct complex comparative genomics, implement HMM searching strategies and conduct phylogenetic analysis on a grand scale while making use of sophisticated phylogenetic methods (for example