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Field
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project deliverables are met. Derivation of closed-form theoretical latency and timeliness expressions for cloud-hosted AI services and edge-assisted offloading strategies. Analysis of theoretical latency
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machine-learning frameworks, Language model customization and agentic AI frameworks, Cloud platforms Written and spoken English proficiency. International candidates that are not exempt from
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with high-performance computing (HPC) environment, cloud-based computation, software tools for genetic epidemiologic analyses (e.g. GWAS, whole-genome sequencing, polygenic score, Mendelian Randomization
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ecosystem, the Fellow will help connect real-time TEM data to cloud-based digital twins and the broader AI framework controlling synthesis and electrochemical testing, creating a closed experimental
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, while integrating real-time data into cloud platforms to develop reliable, secure, and scalable solutions. Main tasks and responsibilities Design, develop, and test IoT solutions for air quality
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biological data. · Proficiency in R and/or Python for data analysis and visualization. · Experience working with large datasets in an HPC or cloud computing environment. · Demonstrated ability to work
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, validation activities and optimisation studies Adapt, extend and maintain modelling, data management and cloud-based exchange systems to enable effective collaboration and delivery across the IGNITE Hub
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· Working knowledge of NCBI, Ensembl, UCSC, UniProt, and other genomic databases · Additional assets: Experience with high-performance computing clusters or cloud computing, bioinformatics workflow
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at scientific edge systems using large-scale HPC/AI computational and storage systems. Design and evaluation of ephemeral, user-configurable, and composable data and storage systems. Evaluation of cloud data
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. Knowledge of cloud-based computing platforms for data processing (e.g., AWS, Google Cloud). Understanding of BMS architecture and electric mobility systems.