Overview
We seek a Ph.D. student on the subject of ‘AI-enhanced time-critical computing and adaptation for distributed data-centric applications.’ The candidate will tackle the computing challenges in the use cases of digital twins, essential climate valuable workflows, and large-scale big data pipelines provided by research communities of environmental and earth sciences through ongoing projects LTER-LIFE, ENVRI-HUB next, and EVERSE. The candidate will research and develop AI-enhanced advanced algorithms for enabling quality critical applications, e.g., digital twins, on heteronomous computing infrastructure (e.g., from edge to hybrid and federated cloud environments), focusing on scheduling, optimization, performance diagnosis, and adaptation challenges of the application lifecycle from composition, deployment to runtime adaptation. The candidate will be encouraged to try state-of-the-art AI approaches to design the algorithms and obtain explainable findings.
The researchers will be embedded in the MultiScale Networked System (MNS) group, Informatics Institute at the University of Amsterdam. The candidate will work in an interdisciplinary team with domain scientists, modelers, and data curators to tackle the software challenges in developing virtual research environments and infrastructure services for constructing specific digital twins required by the ecological research domains.
What are you going to do?
The researcher will focus on the computing challenges in developing quality critical distributed data-centric applications. Working in an interdisciplinary team, the candidates will
analyze the performance challenges of distributed data-centric applications provided by application domains
review the state-of-the-art and technical gaps for developing quality critical distributed data-centric computing systems with a specific focus on AI and machine learning-based approaches,
research, develop, and validate scheduling, optimization, and adaptation algorithms for distributed data-centric computing systems via use cases and state-of-the-art baselines.
Company:
University of Amsterdam
Qualifications:
The candidate should have a master degree in computer science or relevant disciplines and fluently speak and write English. Specifically, we are interested in people with:
expertise in cloud computing, distributed and parallel computing, and data-centric applications,
be familiar with AI (machine learning), big data, and data management techniques,
be experienced in programming (using Java, Python or other languages),
be willing to learn new theories, methods, and technologies when needed for the project.
Educational level:
Master Degree
About University of Amsterdam
The University of Amsterdam is a public university located in Amsterdam, Netherlands.
