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opportunities for these diseases. The project will use a wide variety of data processing, data analysis, and statistical techniques to functional genomic data (ChIP-seq, RNA-seq, ATAC-seq, co-accessibility
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performance of novel proteins and coordination with UK and Japanese partners to implement the platform technology. You should possess a PhD or PhD-equivalent work experience in the field of synthetic biology
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synaptic function and synaptic loss, and keep meticulous, detailed records of your work and commit to engaging with cloud-based analyses on the IMCM data platform. Other duties will include collaborating
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of progression to secondary acute myelogenous leukaemia (sAML). You will take a lead on developing data analysis approaches to search for targetable genetic, epigenetic, or epitranscriptomic mechanisms
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research materials and analysing and presenting qualitative and/or quantitative data from a variety of sources. The successful candidate will hold a PhD degree in a relevant topic (e.g. Biomedical sciences
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of RNAseq data and in vivo modelling of cancer. In addition to leading your own research, you will provide guidance and support to junior members of the lab, including PhD and project students, research
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responsible for managing your own academic research and administrative activities and adapting existing and developing new research methodologies and materials. You will analyse quantitative data from a variety
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of data and report writing. This role also includes in collaboration and preparation of research publications, presenting findings and acting as source of information and advice to other members
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expected that the successful candidate will contribute to the computational analysis of multi-omic data that will be generated during the project. The candidate must hold or be near completion to a PhD/DPhil
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developing data analysis approaches and contribute to the overall study design and implementation. In particular, this will involve applying existing or even developing new algorithmic approaches for TAPS data