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Field
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PhD Expert knowledge of statistical modelling, statistical inference and use of relevant software (e.g. R/Python) both for data processing, visualisation and programming. Excellent communication and
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and data analysis tools commonly used in bioinformatics (e.g., shell scripting, Python, R). Solid understanding of omics data, including metagenomics, RNA-seq, and metabolomics. Experience with
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desirable. Proficient in Python, R, and ML libraries such as PyTorch or TensorFlow. Strong communication and collaboration skills; ability to work independently and as part of a team. Willingness to respect
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techniques. Training or demonstrated experience in remote sensing, spatial data collection, and thematic mapping. Proficiency in data analysis software (e.g., R, MATLAB, SPSS, Primer, Python). Experience with
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years and in the relevant areas of Machine Learning / Artificial Intelligence, Credit Risk Modeling and Operations Optimization Modeling; The candidate must have strong programming skills in Python, and
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. Proficiency in data analysis software (e.g., R, MATLAB, SPSS, Primer, Python). Experience with analytical techniques such as ANOVA, PCA, Regression, and Multidimensional Scaling (MDS). Experience in ecosystem
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artificial intelligence, as well as knowledge in programming languages and libraries such as Python, PyTorch (or similar), and Pandas/Darts/Prophet/sktime or similar. Taking into account paragraphs 1 and 2
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machine learning and statistical analyses. Proficiency with Python and relevant libraries. Prior experience with genomic data modeling. Interest or experience working in neuroscience domain applications
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packages relevant to biostatistics and bioinformatics (e.g. R, Python, Fortran, Matlab, SAS, Stata or equivalent) and experience in the use of high-performance computer resources and parallel computation
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and/or quantum photonics Experience in nano/micro-fabrication Programming skills (e.g., Python, Matlab) Ability to work as a team and communicate effectively More Information Location: [[Kent Ridge