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Faculty of Life Sciences and Medicine Hub for Applied Bioinformatics). We are looking for an ambitious candidate with established expertise in bioinformatics, specifically dealing with large data sets and
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transcriptomics data analysis and interpretation Desirable criteria Contribution to open-source bioinformatics tools Hands-on experience with machine-learning frameworks Downloading a copy of our Job Description
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Desirable criteria Experience of advanced statistical and/or machine learning methods, such as longitudinal analysis methods, latent variables models, clustering algorithms, missing data and clinical trial
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for Research Staff Development for more information. About You To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD qualified in
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Medicine Hub for Applied Bioinformatics). We are looking for an ambitious candidate with established expertise in bioinformatics, specifically dealing with large data sets and data integration. Knowledge
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transcriptomics data analysis and interpretation Desirable criteria Contribution to open-source bioinformatics tools Hands-on experience with machine-learning frameworks Downloading a copy of our Job Description
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transcriptomics data analysis and interpretation Desirable criteria Contribution to open-source bioinformatics tools Hands-on experience with machine-learning frameworks Downloading a copy of our Job Description
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the role Overview of the role We are seeking a highly motivated Research Fellow in Machine Learning to join the PharosAI team, focusing on developing novel machine learning methods in computer vision
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. You will also be responsible for implementing the model as a computer simulation and analysing it within a health-economics framework using standard computational techniques. You will also be
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for implementing the model as a computer simulation and analysing it within a health-economics framework using standard computational techniques. The post-holder will also be responsible for writing up the findings