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experience and evidence against the published criteria. Ideal candidate: The successful candidate will have a PhD (awarded or expected within 6 months) in Computer Science, AI, Machine Learning, Computational
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generation for future funding Participate in laboratory meetings, journal clubs, and collaborative program activities Contribute to mentoring of junior trainees Other duties as assigned Doctoral degree (PhD
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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. Experience with high-performance computing clusters or cloud-based Earth observation platforms (e.g., GEE Python API). Experience with airborne lidar data processing and canopy height modelling. We will place
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: Programming proficiency in at least R or Python (ideally both), plus comfortable use of Unix/Linux shell. Hands-on experience with high-performance computing (Slurm/PBS or equivalent) and/or cloud computing
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at scientific edge systems using large-scale HPC/AI computational and storage systems. Design and evaluation of ephemeral, user-configurable, and composable data and storage systems. Evaluation of cloud data
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description here About you You will have: PhD (or in the final stages of PhD submission) in computational biology, evolutionary genomics, computer science, or a related field* Strong analytical, quantitative
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Oden Institute for Computational Engineering and Sciences | Austin, Texas | United States | 2 months ago
will be encouraged to develop independent research questions within this broader program. Required Qualifications PhD (or equivalent) in statistical genetics, computational biology, biostatistics
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quality control of computational workflows. Contribute to manuscript writing and dissemination of research findings. Qualifications: PhD in Bioinformatics, Computational Biology, Systems Biology, Statistics
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degree (PhD, MD/PhD, or MD) in computational biology, bioinformatics, biostatistics, cardiovascular biology, epidemiology, or a closely related field Experience with proteomic, metabolomic, or other omic