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sufficient specialist knowledge in transcriptomics and NGS sequencing as well as the essential computational skills (R, Unix or Python) to develop research programmes in this area, and familiarity with
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will be required to take a leading role across multiple projects, with support from other members of the team. Project topics are agreed with the funders in accordance with their priorities. KIS is a KHP
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with sequencing bacteria in the laboratory. Key attributes of the successful applicant include: Experience with REDCap: setting up databases Experience of working in a healthcare setting, medical research
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, the development and fine-tuning of vision foundation models, multiple instance learning, survival analysis, and interpretable model development. You will also lead efforts in building multimodal deep learning
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with sequencing bacteria in the laboratory. Key attributes of the successful applicant include: Experience with REDCap: setting up databases Experience of working in a healthcare setting, medical research
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, including integration of multimodal sequencing and 3D imaging datasets. They will be expected to produce independent and original research in this area, develop and apply new concepts and have a creative
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which they operate. We have established collaborations across the globe and built a research programme that leverages high throughput, high-resolution sequencing platforms and novel analytical methods
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. The researcher will use protein sequence and structure-based features along with protein language models to develop AI based predictors. The final stage will involve developing a diagnostic portal to identify
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planning and delivery of independent research. You will be expected to identify gaps and research opportunities aligned with both the Department’s portfolio of research activities and broader industrial
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(£2.5M in total) and aligned to the NIHR Applied Research Collaboration (ARC) South London as co-funded research infrastructure. The KIS programme will continue to support scalable projects and programmes