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functioning or ecological stability; experience with remote-sensing products and cloud/geospatial computing is an advantage; a publication record that is competitive for the candidate’s career stage
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algorithms for analysis of large image datasets. Experience with parallel computing, high-performance computing, distributed computing, and/or cloud-based computational workflows. Experience with large-scale
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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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model predictive control, machine learning, or reinforcement learning. Experience with IoT, edge computing, and cloud integration. Proven ability to contribute to research funding proposals
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, including most of the following: working with public databases, assembly, annotation, binning, SNV profiling, and distributed computing infrastructure (cluster and/or cloud). An interest in learning
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systems ● PhD in Computer Science, Scientific Computing, Data Science, Informatics, AI/ML, or a closely related area, or equivalent research experience. Preferred skills and experience: ● Experience
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with high-performance computing, GPU programming, parallel programming, cloud computing, and/or related methods including running numerical simulations of complex workflows. Demonstrated technical
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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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Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences（CAS) | R P Agraharam, Andhra Pradesh | India | about 20 hours ago
categories View less categories Discipline Applied Science , Biomedicine , Chemistry , Clinical , Computing , Engineering , Environmental Science , Health Science , Life Science , Mathematics
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). We are looking for a person with: A PhD in computer science or a related field, with a software systems profile: distributed systems, cloud or edge computing, simulation, digital twins, model-based