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Field
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, FEDER and FCT, is available under the following conditions: OBJECTIVES | FUNCTIONS Cloud computing is currently in an impasse. While hardware efficiency is improving at an exponentially lower rate, the
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. Operating persistent or ephemeral services supporting workflows, such as databases, workflow engines, cloud-native frameworks, AI inference front ends, and REST APIs. Coordinating dynamic service deployments
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or ephemeral services supporting workflows, such as databases, workflow engines, cloud-native frameworks, AI inference front ends, and REST APIs. Coordinating dynamic service deployments and specifying storage
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and control conditions. Create and manage data pipelines for real-time quality assurance, cloud-based storage, and automated analytics. Collaborate with faculty, students, and community partners
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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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for cloud-hosted AI services and edge-assisted offloading strategies. Analysis of theoretical latency and timeliness for cloud-hosted AI services and edge-assisted offloading strategies. Design and
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TEC. 2. OBJECTIVES: - broaden knowledge of the state of the art in the scientific field of DevOps Cloud Architectures, Infrastructure as Code (IaC) and on-demand, intelligent configuration of self
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measures against common threats like DDoS attacks, SQL injection, and cheating. Experience in multiplayer game services such as Azure PlayFab, AWS GameLift, Google Cloud Servers, and other similar services
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analysing of point cloud data mainly from airborne LiDAR (ALS) but potentially also from terrestrial and mobile sources (TLS & MLS). The goal of the project is to uncover the efficacy of using airborne LiDAR
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bioinformatics tools and libraries (e.g., Bioconductor, STAR, DESeq2, Seurat) and familiarity with cloud computing platforms and scalable computing infrastructures for large datasets. How to Apply: Interested