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About: PhD Position in Deep Learning for Artificial Intelligence of Things; Two Ph.D. fellowships or scholarships in the fields of electrical and computer engineering are available through the Graduate School of Technical Sciences at Aarhus University in Denmark.
The roles are associated with the Horizon Europe project PANDORA and the NordForsk project Nordic University Cooperation on Edge Intelligence (NUEI), with an effective date of May 1, 2024, at the latest.
With potential for networking and collaboration across European institutions, the research focuses on deep learning for Artificial Intelligence of Things (AIoT).
Research topic and Project overview: We are seeking submissions for two PhD posts supported by the NordForsk project Nordic University Cooperation on Edge Intelligence (NUEI) and the Horizon Europe project PANDORA.
The two chosen candidates will have excellent chances to collaborate, engage, and build relationships with scientists, engineers, and researchers employed by European universities, research institutes, and consortium-forming businesses.
The PhD candidate also has the chance to take advantage of NUEI’s PhD courses and summer schools.
The primary goal of the first PhD research is to suggest novel Deep Learning models for effective inference from input streams related to the Internet of Things.
Real-time processing of streams such as image sequences and sensor time-series can be gathered by sensors.
In order to facilitate the creation of highly optimized inference pipelines capable of executing complex models with low latency and high throughput, we will expand upon our recently introduced Continual Inference Networks, which consist of the Continual 3D Convolutional Neural Networks, the Continual Transformer Encoders, and the Continual Spatio-Temporal Graph Convolutional Networks.
In the IoT-Edge-Cloud continuum, the second PhD research intends to create unique methodologies for distributed AI learning and inferencing.
Objective: The objective of this project is to effectively handle the trade-off between resource utilization, inference accuracy, time, and energy consumption throughout the continuum by adjusting to the ever-changing dynamics of data processing environments and communication networks.
Aim: Our aim is to enhance AI learning and inferencing in the IoT-Edge-Cloud continuum by proposing and creating innovative algorithms for load balancing, energy management, and dynamic task distribution.
The chosen candidates will engage with the project collaborators as part of the two PhD projects, disseminate knowledge by attending conferences and submitting project reports to showcase their work, and have the chance to take part in the projects’ activities.
Study Area: This research includes unique methodologies for distributed AI learning and inferencing across the IoT-Edge-Cloud continuum, as well as deep learning for efficient inference of AIoT input streams and real-time sensor-based data processing.
Scholarship Description: The first doctoral study employs continuous inference networks to propose novel deep learning models for effective AIoT inference.
The second project aims to optimize resource utilization, inference accuracy, and energy consumption by developing approaches for distributed AI learning and inferencing in the IoT-Edge-Cloud
Qualifications: Candidates need to possess the following:
- Master’s degree in computer science, computer engineering, or a related field.
- Experience in machine learning.
- Strong programming abilities in Python.
- Outstanding written and verbal English abilities.
- The capacity to function well in a multidisciplinary team.
- Expertise in deep learning, wireless communications, or video processing is advantageous.
Required Document Candidates need to send in:
- Curriculum vitae
- A motivation letter detailing the project’s research plans
- List of works published, if any
- Academic transcript or education certificate
- A recommendation letter
Last day to submit applications is February 15, 2024, at 23:59 CET.
A preferred start date of May 1, 2024 is indicated. See the application guide for information on required attachments and application requirements. Interview invitations may be extended to shortlisted candidates. Aarhus University encourages diversity and equality in the workplace.
Note: You should outline your concepts and research strategies in this paper for this particular project. You have the option to provide a URL where more information can be obtained.
