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Field
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. In parallel with your experimental work, you would develop theoretical models in collaboration with our international collaborators. You would also develop advanced image analysis schemes to analyse
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2019 https://doi.org/10.1038/s41596-018-0110-x and Lara et al., arXiv:2503.21396 https://doi.org/10.48550/arXiv.2503.21396 )). In parallel with your experimental work, you would develop theoretical
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meet security, compliance, and integration requirements. You will collaborate with various professionals and act as the link between technical teams, clinical teams, and project management. In parallel
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing
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McGill University | Winnipeg Sargent Park Daniel McIntyre Inkster SE, Manitoba | Canada | 3 months ago
imaging system based on the random access parallel imaging (RAP) system platform (https://doi.org/10.7554/eLife.56426 ). The RAP platform quickly collects high-contrast images in multi-well plates using
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with Python and R programming languages. Experience with functional genomic technologies including massively parallel reporter assays. Biomedical informatics or biomedical research experience. Preferred
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than 30 people, including parallel post-doctoral researchers in four other countries, and led by Prof Ben Baumberg Geiger at King’s. Secondly, it’s located within the Centre for Society and Mental Health
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lattice field theory and numerical methods, with experience in HPC programming (e.g., C++, Python, MPI, OpenMP, CUDA) and parallel computing environments. - Experience in performance analysis, debugging
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of this position is to develop a massively parallel version of a computer code called Commander, and apply this to archival data from Planck HFI, new data from Simons Observatory, and simulated data from LiteBIRD, a
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frameworks (e.g. PyTorch, TensorFlow) and relevant libraries. Practical experience in scalable data processing, including the use of parallel computing, cloud platforms, and distributed systems for efficient