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biological interpretation. · Analyze single-cell RNA-seq datasets, including QC, normalization, integration/batch correction when appropriate, clustering, cell type/state annotation, differential
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environments, token-based data-access infrastructures, and next-generation HTTP/S caching technologies. The Lab also maintains the ATLAS distributed analytics and AI-assisted observability and operations
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Southeast Asia, including Singapore. Each team of 6-7 students is supervised by a lecturer who specializes in the area they are working with. Students would choose to work with lecturers clustered into three
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Orchestration Services Familiarity with HPC Cluster Administration and CI/CD tools (Azure DevOps) Experience with Amazon Web Services, Microsoft Azure, or Google Cloud Platform (AWS CloudFormation or Azure
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/unsupervised learning (regression, classification, clustering), ensemble methods, and deep learning architectures (CNNs, RNNs). Experience with explainable AI (e.g., SHAP, LIME) and radiomics preferred
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. Collaborative design and optimizing of the in vitro enrichment protocol for next generation sequencing library preparations, 2. Annotation of biosynthetic gene and cluster subsequences, 3. Analysis of greenhouse
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with excellent facilities for protein science research. There will be direct access to advanced biophysical infrastructure in the biophysics core facility headed by the PI, a GPU cluster with working
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cluster hire in support of the new Lab. The candidates will interact with other research teams at the Lab as well as faculty members at UNR and other partnering institutes and agencies. The Lab is committed
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Metallurgy and Corrosion cluster, working within a multidisciplinary team spanning theory, advanced characterisation, and computational modelling. This environment provides an excellent platform for developing
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the area of machine learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement tasks to work