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applications for the position of Research Assistant. Key Responsibilities: Study and analyze dataflow patterns in emerging data-intensive applications. Design and develop parallel file systems for node-local
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programming language to solve computational problems Awareness of computational infrastructure and its upkeep Ability to work on multiple projects in parallel and set priorities Willingness to provide training
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to computational problems Ability to code in a programming language to solve computational problems Awareness of computational infrastructure and its upkeep Ability to work on multiple projects in parallel and set
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solve computational problems Awareness of computational infrastructure and its upkeep Ability to work on multiple projects in parallel and set priorities Willingness to provide training to staff and
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. Programming & Software Development: Proficiency in Python, PyTorch, JAX, or other ML frameworks Computing: Some experience with large-scale datasets, parallel computing, and GPUs/TPUs. Algorithm Development
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-related issues in organizational strategic decision processes. This role integrates behavioral research approaches, field studies, and data analysis to generate high-quality academic research outputs and
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learning algorithms to support research in IDMxS. Key Responsibilites: Apply/ improve/ develop machine learning algorithms to process (e.g., classify, predict) data/ images collected by IDMxS. Help supervise
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CO2 removal research. Key Responsibilities: Electrochemical process on interface phenomena MOF synthesis, testing under different conditions Simulation of scaled up process. Interface with machine
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effectiveness of battery recycling workflows, supporting experimental and simulation studies across the battery lifecycle, and implementing algorithms for process monitoring and optimization so that recycling
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systems, with a focus on thermal integration of metal hydride storage systems. The Research Fellow will develop CFD models, process simulations, and system-level integration strategies to enable efficient