4 of 4 Permanent Convolutional Neural Network Jobs in Norfolk

problems with numerous and complex variables - Essential Experience in applying machine learning solutions for genomics investigations - Essential Requirement: Experience using machine learning applications (e.g. convolutional neural networks, recurrent neural networks) - Essential Experience of working on externally funded projects - Essential Experience with next generation sequencing (RNA-Seq, DNA … based on integration of multi-omic data (genome, transcriptome, epigenome) and integrate this novel information in the development of tissue/condition specific regulatory network, with the aim of prioritising variants occurring within those regions based on their predicted impact. They will also lead on the analysis, preparation, and … submission of the associated manuscripts. The successful candidate will develop computational pipelines centered on machine learning applications such as convolutional neural network to reproducibly handle multi-omic data (genome, transcriptome . click apply for full job details More ❯

based on integration of multi-omic data (genome, transcriptome, epigenome) and integrate this novel information in the development of tissue/condition specific regulatory network, with the aim of prioritising variants occurring within those regions based on their predicted impact. They will also lead on the analysis, preparation, and … submission of the associated manuscripts. The successful candidate will develop computational pipelines centred on machine learning applications such as convolutional neural network to reproducibly handle multi-omic data (genome, transcriptome, epigenome) to enable functional sequence annotation, network reconstruction, and the interpretation of the implication of genetic More ❯

based on integration of multi-omic data (genome, transcriptome, epigenome) and integrate this novel information in the development of tissue/condition specific regulatory network, with the aim of prioritising variants occurring within those regions based on their predicted impact. They will also lead on the analysis, preparation, and … submission of the associated manuscripts. The successful candidate will develop computational pipelines centred on machine learning applications such as convolutional neural network to reproducibly handle multi-omic data (genome, transcriptome, epigenome) to enable functional sequence annotation, network reconstruction, and the interpretation of the implication of genetic More ❯

based on integration of multi-omic data (genome, transcriptome, epigenome) and integrate this novel information in the development of tissue/condition specific regulatory network, with the aim of prioritising variants occurring within those regions based on their predicted impact. They will also lead on the analysis, preparation, and … submission of the associated manuscripts. The successful candidate will develop computational pipelines centred on machine learning applications such as convolutional neural network to reproducibly handle multi-omic data (genome, transcriptome, epigenome) to enable functional sequence annotation, network reconstruction, and the interpretation of the implication of genetic More ❯