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computational approaches, including in vivo Massively Parallel Reporter Assays (MPRAs), to define the sequence basis and functional consequences of enhancer activity and to expand MPRA-based approaches to other
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simulation runtimes. During the PhD programme, a student would be expected to explore and analyse the limitations of possible solutions for parallel simulation of massively multicore computers. During
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: Recent PhD and/or MD in a relevant field, or equivalent research experience, with a background in molecular biology, transcriptional regulation, functional genomics, computational biology, genetics
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We are looking for a highly motivated candidate to pursue a PhD programme titled "CFD-informed finite element analysis for thermal control in wire-arc directed energy deposition." This research
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at least one of: High-Performance Computing Distributed Systems Parallel Computing High Performance Algorithms Multiple Linux distributions · Experience with SLURM or similar HPC schedulers
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physics Physics » Thermodynamics Computer science » Modelling tools Researcher Profile First Stage Researcher (R1) Positions Postdoc Positions Application Deadline 19 Apr 2026 - 23:59 (Europe/Brussels
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23 Feb 2026 Job Information Organisation/Company Centrale Lille Institut Department North Research Field Engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions
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State University of New York University at Albany | Albany, New York | United States | about 1 hour ago
vibration isolation). In parallel to the benefits of quantum computing, artificial intelligence and neuromorphic computing seek to emulate the massively parallel, highly efficient computing capacity
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, for example: Large‑scale optimization and machine learning: Stochastic and/or (non‑)convex optimization methods, first‑order methods, variance reduction, distributed and parallel optimization, federated
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: Large‑scale optimization and machine learning: Stochastic and/or (non‑)convex optimization methods, first‑order methods, variance reduction, distributed and parallel optimization, federated learning