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tightly coupled to GPU-accelerated high-performance computing using Lund University’s national e-infrastructure. You will join an interdisciplinary research environment that values scientific independence
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principles and research data management. Experience with high-performance computing, cloud computing, GPU acceleration, or distributed data processing. Experience participating in international scientific
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cell- and module level. Particular emphasis will be placed on leveraging modern heterogeneous computing architectures to enable large-scale, high-fidelity reacting-flow simulations. The models developed
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activities in areas such as numerical analysis, mathematical modelling, development and analysis of algorithms, machine learning, optimization, scientific software development and high-performance computing
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partners. Tasks and responsibilities You will conduct computational research involving Python programming and high-throughput protein structure prediction using high-performance computing infrastructure
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areas such as machine learning, optimization, scientific software development and high-performance computing. The division is an important part of the eSSENCE e-science collaboration and of the Science
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sequence alignments. In this project, we will conduct a comparative study of noncoding genomic regions. You should have expertise in algorithm design and high-performance computing, particularly in
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experience with R and/or Python language, bash shell environment, high performance computing, next-generation sequencing data analysis, and single-cell analysis workflows 2 – Communication and team work
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with: Experience with web-GIS development or geospatial data visualization. Experience with high-performance computing clusters or cloud-based Earth observation platforms (e.g., GEE Python API
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Linköping University, and aims to understand the cybersecurity challenges in software-intensive and high-performance communication networks using AI-powered methods. We will advance the research front