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datasets to uncover microbiome–host interactions Strong statistical skills in Python and/or R Experience with cloud computing and high-performance computing environments Ideal Candidate Profile We are
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, data analysis, and visualization environments/IDEs (e.g., R/R Studio, Python/Spyder/VSCode) Familiarity with GIS methods and cloud-based computing resources (e.g., Google Earth Engine) for large-scale
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Applying machine learning frameworks to satellite datasets Cloud computing Modes of Work Positions that are eligible for hybrid or mobile/remote work mode are at the discretion of the hiring department
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schedulers (e.g., SLURM or qsub); experience with cloud computing platforms (e.g., AWS , GCP , Terra) is a plus · Experience developing and training deep learning models, ideally with fluency in
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policy, agronomy, or a related field Desired Qualifications Strong skills in satellite remote sensing, GIS, and statistical analysis. Experience using cloud-based computing resources (e.g. Google
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using Snakemake, Nextflow, or similar platforms. • HPC or cloud computing environments. • Long-read or single-molecule sequencing, computational epigenomics, and/or 3D genome analysis. • Statistical
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-time inference within rendering pipelines Minimum Qualifications: • Ph.D. in Computer Science, Visual Computing, Interactive Media, or related field • Strong C++ and/or C# programming expertise
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researchers at the College of Computing and Data Science (CCDS) at NTU. Technical skills, natural curiosity, versatility, and enthusiasm are highly desired traits that will enable success in this fast
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biology, biomedical data science, or related analytical methodologies is highly desirable. Experience with software development, cloud computing, data engineering, algorithm design, or scalable
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Python and/or R; familiarity with high-performance and cloud computing environments. • Experience in at least two of: phylodynamics / genomic epidemiology, deep learning for sequence or tabular data