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Field
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, R) and geospatial tools (e.g., ArcGIS, QGIS ), spatial statistics, or cloud computing Experience working in collaborative research teams Valid driver’s license and be able to successfully pass a
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contribute to analyses and processing of data. They will become familiar with high performance computing environments and parallel processing, as well as cloud computing infrastructure. They will
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preferred. The Department of Computational Biology provides access to high-performance computing clusters, a cloud computing environment, innovative visualization tools, highly automated analytical
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independence, good documentation habits and a track record of productive scientific results Preferred Qualifications Equipment Utilized High-performance computing; cloud computing; local workstations. Physical
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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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, multivariate methods, or comparative and phylogenetic analyses. · Experience using high-performance computing (HPC), cloud computing, or large-scale machine learning workflows. · Familiarity with open
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independence, good documentation habits and a track record of productive scientific results Preferred Qualifications Equipment Utilized High-performance computing; cloud computing; local workstations
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with advanced characterization tools (e.g., PPMS, XRD, SQUID magnetometry, optical spectroscopy, or scanning probe microscopy). Experience with high-performance computing or cloud-based machine
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, ecology, microbiology, engineering, bioinformatics, data mining, parallel programming, supercomputing, or cloud computing. Application Instructions How To Apply Interested individuals should send
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. Familiarity with causal inference, real-world evidence, multimodal data integration, or cloud/high-performance computing is a plus. The Postdoctoral Researcher will collaborate closely with