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Field
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integration Key areas: § Metagenomic and 16S rRNA sequencing analysis § Single-cell RNA-seq and proteomic/metabolomic data integration § Machine learning and AI applications in microbiome data
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machine learning is essential; while structure prediction or materials chemistry experience would be advantageous, it is not a pre-requisite for the role. This post would be ideal for an ambitious and
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-quality research publications in econometrics or related fields. You will possess the ability to solve complex statistical problems, show advanced computer literacy with proficiency in relevant software
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or equivalent. • Have relevant research/industry experience in Machine Learning, AI and Privacy. • An excellent team player who can cope with an agile and fast-paced environment. • Good communication
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qualification/experience in a related field of study. The successful applicant will have expertise in statistical modelling, epidemiology or machine learning and possess sufficient specialist knowledge in
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to work independently and collaboratively. Advanced planning, time management, and written communication skills are essential, along with proven computer literacy and proficiency in relevant software and
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, written, and oral communication skills in English. Exhibit strong organisational skills and the ability to meet deadlines and complete projects. Have expertise in machine learning and/or programming (highly
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machine control for smart manufacturing as well as publications in top-tier international conferences and journals, as well as real-world implementations. If interested, please apply with your resume
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SUMOylation, transcription factors, or chromatin dynamics. Expertise in machine learning or statistical modeling for biological data. Knowledge of enhancer-promoter interactions and 3D genome organization. All
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Educational Technology in the School of Computer and Mathematical Sciences. The successful candidate will be a researcher in the use of technology to support cognitive and meta-cognitive skills of students