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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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Department: Geography Vacancy ID: 040283 Closing Date: 22-Mar-2026 Maynooth University is committed to a strategy in which the primary University goals of excellent research and scholarship and
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3 Mar 2026 Job Information Organisation/Company MAYNOOTH UNIVERSITY Research Field Geography Researcher Profile Recognised Researcher (R2) First Stage Researcher (R1) Application Deadline 22 Mar
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will encourage their implementation, especially by using parallel computing. * Assigned department Existing departments [Work location] * Address 606-8501 Kyoto Minato Laboratory, Graduate School
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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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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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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