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, vascular biology, or cardiometabolic disease Track record of peer-reviewed publications relative to career stage Experience with multi-omic data integration Familiarity with HPC or cloud computing
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optimization Familiarity with continuous-variable/bosonic systems, non-Gaussian states, multimode entanglement, or Wigner-function methods Experience with HPC workflows (cluster computing, GPU computing
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for reproducible HPC environments. Experience with CUDA-level optimization or debugging hardware-specific performance differences. Basic knowledge of protein structure, folding, or biophysics. Our University
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or single-cell genomics analysis. Familiarity with graph neural networks, transformers, generative AI or foundation models. Experience working with cloud/HPC environments, workflow orchestration
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experience in working with Linux HPCs · Experience in applying machine learning methods to genomics data analysis · Experience in navigating public databases and genomics data repositories
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Experience with HPC workflows (cluster computing, GPU computing, reproducible pipelines) Track record of collaborative work across theory/experiment or interdisciplinary teams All candidates and projects will
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, metabolomics, proteomics) is an asset. Knowledge of data harmonization platforms (e.g., Maelstrom Research guidelines) is an asset. Experience with high-performance computing environments (Unix/HPC clusters) is
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cloud platforms for compute and storage. Version Control & CI/CD: Git, automated testing, deployment workflows. Experience with Linux systems, HPC, and distributed computing environments. Knowledge
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scientific software development. Proficiency in C/C++ and Python, with experience in HPC environments (e.g., MPI/OpenMP; GPU experience a plus). Record of peer-reviewed publications appropriate to career stage
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can take up the position. Good oral and written presentation skills in English equivalent level C1 Experience in analyzing large-scale next-generation sequencing datasets in Linux/HPC environments using