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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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Your Job: This PhD project bridges between classical analytical methods and modern AI based techniques to analyse spike train recordings to advance our understanding of neural population coding while maintaining clarity in the interpretation of results. Concurrently, AI-based methods are...
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related in space and time and to behavioral events. Core Tasks: Getting familiar with the experimental data and the concepts of neuronal coding, and Elephant Analysis of the parallel rate data for
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: One-off Quantity: Multiple Eligibility and selection criteria Eligibility To be eligible for this grant, you must: Be an Aboriginal and/or Torres Strait Islander person, identify as Aboriginal and/or
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strong background in applied mathematics Excellent programming skills (Python, C/C++) Good experience in machine learning and parallel computing Good organisational skills and ability to work both
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*. * The Employment Equity Act, which is under review, uses the terminology Aboriginal peoples and visible minorities. Candidates are asked to self-declare when applying to this hiring process. City: Calgary, Edmonton
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on the “Apply Online” button on the top right-hand side of this page Step 2: Complete Complete the application form online Application form can be saved at any point of the process Multiple scholarships can be
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grant support service processes. Works closely with Arts Development team to identify and determine suitability of specific foundation funding opportunities and approaches, and to ensure that foundation
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of telecommunications and are undertaking travel related to their research. Benefits This scholarship provides a one-off payment of approximately $5000. Amount: Up to $5,000 Duration: One-off Quantity: Multiple
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Infrastructure? No Offer Description No. 1 scholarship grant for scientific training activities at INFN Structure of Perugia for the following research topic: “Techniques for collecting and analyzing multiple