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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 14-May-25 Location: Philadelphia, Pennsylvania Type: Full-time Categories: Academic/Faculty Computer/Information
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with the architecture and performance characteristics of distributed computing and data handling systems. Extensive knowledge in computer science or related field, demonstrated through education or
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based on MPI. Experience working with the architecture and performance characteristics of distributed computing and data handling systems. Extensive knowledge in computer science or related field
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reviewed • Understand the product thoroughly; Analyse, design and develop functionalities based on product requirements • Work with researchers to implement and develop parallel and distributed systems
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, copyrighted, or biased. By studying brain data recordings and building computational models that mimic real populations of neurons, the project aims to uncover active unlearning: how the brain learns
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algorithms and complexity theory, including in both well-established settings (e.g., sequential computation on a single machine and distributed/parallel computation on multiple machines) as well as emerging
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projects will work towards this goal. PhD research fellow will be part of the PhD programme in Computer Science: Software Engineering, Sensor Networks and Engineering Computing (https://www.hvl.no/en
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Bayesian approach (Lages, 2024). Techniques used: Computational modelling, Bayesian inference, sampling and simulation techniques, prior distributions and posterior predictive checks, model comparison
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Martian meteorite falls using advanced correlative microscopy techniques. To determine if they are the same or different Methods We will use a correlative, big data approach that combines X-ray computed
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: Understand the relationship between POPs and the glacial environment: POPs will be characterised and quantified within different glacial substrates (snow, ice, water, cryoconite, sediments) and within