The Advanced Network Management and Control laboratory at Electrical Engineering (EE) department of TU/e has two PhD positions in the field of edge network and computing resource allocation using artificial intelligence at the edge (Edge AI).
PhD 1 - Edge Network Resource Availability Prediction
PhD 2 - In-network Distributed Resource Planning
Goal and background
Computing resources available at or nearby a cyber-physical system have much slower upgrade cycles than the algorithms they serve. This makes maintenance very expensive as physical access by experts becomes necessary for upgrades or complete replacements. TU/e , ASTRON and Thermo Fisher Scientific join forces to address the problem exploiting novel AI architectures at the edge, like distributed reservoir computing. The project, Autonomous Distribution Architecture on Progressing Topologies and Optimization of Resources (ADAPTOR - ref.18651), was funded by the Open Technology Program (OTP) of NWO Domain Applied and Engineering Sciences (TTW).
ADAPTOR extends the autonomy of these systems by building an intelligent fog/edge solution able to aggregate all resources of interconnected devices into a single distributed pool and assign tasks to it. The goal of ADAPTOR is to accurately predict the availability of resources at any moment and move the task to the best resource. The higher the resource utilization the more effective ADAPTOR solution is. ADAPTOR’s diverse use cases, electron-microscope farms and space-based radio telescope swarms, bring challenges on time-sensitivity, tight resource constraints and scalability.
Requirements derived from the two use cases drive the research challenges of the project. Both cases refer to networks of resources at the edge operating within some kind of isolation. In electro-microscope farms, cost and latency requirements make access to cloud resources prohibitive. Similarly, space-based radio telescope swarms cannot afford data offloading to nearby stationary computing resources, e.g. on Earth, due to power constraints and topology dynamics. It is therefore necessary to harvest unused resources within the farm or swarm.
The project will develop novel distributed AI mechanisms able to predict over time the availability of nearby resources (network and compute). Task migration can then start proactively improving resource utility.
PhD 1 will analytically and experimentally study the problem of scheduling local and remote compute/storage and network resources for a limited number of resource locations and workload sources.
PhD 2 will devise efficient and accurate distributed AI mechanisms. This topic will investigate scalable ways of mapping the ADAPTOR architecture to physically distributed devices. In brief:
The work is strongly experimental on simulated and real-world distributed computing systems.
Eindhoven University of Technology (TU/e) is one of Europe’s top technological universities, in the heart of one of Europe’s largest high-tech innovation ecosystems - the Eindhoven Brainport region. Research at TU/e is a combination of academic excellence and a strong real-world impact through close collaboration with regional and international high-tech industries.
The candidate will be employed within the Electro-Optical Communications Group (ECO), in particular within the advanced network management and control laboratory. The candidate will strongly interact with the ECO group, which consists of over 70 researchers. This position is embedded within the Center for Wireless Technology (CWT/e) at TU/e which focuses on four programs: Ultra-High Data- Rate Systems, Ultra-Low Power and Internet-of-Things Communication, Terahertz Technology, and Radio Astronomy.
We are looking, therefore, for two strong PhD researchers to:
Collaboration - Continuously interact with other ECO researchers and with ADAPTOR’s partners ASTRON , Thermo Fisher Scientific and other users.
Dissemination - Contribute to the project reporting, scientific publications and other activities related to the preparation of new grant proposals to national and European projects.
Requirements - Capture the requirements of the electro-microscopy farm use case and analytically model the problem.
Approach - Design large-scale centralized resource availability prediction AI engines able to perform under uncertainties and with various applications.
Approach - Design in-built and emergent properties of novel AI models which specialize on separate applications as well as influence each other.
Requirements - Capture the requirements of the telescope swarm use case and study the underlying network dynamics.
Approach - Design scalable distributed resource availability prediction AI engines with high prediction throughput and able to be simultaneously used by multiple applications.
Approach - Harvest underlying physical communication networks’ dynamics to improve distributed AI models.
We are looking, therefore, for two strong PhD researchers who demonstrate:
Do you recognize yourself in this profile and would you like to know more? Please contact
dr. Georgios Exarchakos, g.exarchakos[at]tue.nl.
For information about terms of employment, click here or contact HRServices.flux[at]tue.nl
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