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Postdoctoral Researcher in AI Model Optimization
Il y a 3 mois
Position Overview
This postdoctoral position is supported by the collaborative initiative between Inria and Nokia Bell Labs, focusing on the challenge of Learning Networks.
Key Responsibilities
The primary objective of this role is to investigate the complexities surrounding the placement of AI models within Inference Delivery Networks (IDNs). This task presents a significant optimization challenge, requiring a careful balance between model performance, inference speed, and resource allocation, while also addressing the inherent variability of the network, influenced by user demand and fluctuations in computing and communication resources.
Additionally, we will evaluate other performance indicators, such as energy efficiency, in our optimization goals and network constraints, particularly in scenarios where the network exhibits inelastic characteristics. Our approach will incorporate multi-objective optimization strategies, including Pareto optimal solutions, and transfer learning methodologies to adapt models across nodes with varying resource levels and knowledge.
We will also utilize online learning techniques to ensure model placements are resilient against adversarial conditions. Unlike our earlier studies, this research will explore the possibility of distributing models across multiple nodes, allowing for a comparative analysis of various model splitting strategies, both with and without bottlenecks, focusing on metrics such as inference latency and network utilization.
Our evaluation will extend to environments characterized by significant node heterogeneity, particularly in the context of embedded Edge AI and TinyML, where resource constraints may be drastically lower but the number of devices could be substantially higher.
Collaboration and Supervision
This postdoctoral role will be based at Inria Saclay, under the guidance of a collaborative team comprising members from Tribe (INRIA), Neo (INRIA), and AIRL (Nokia).
Main Activities
- Conducting comprehensive literature reviews and synthesizing relevant research.
- Engaging in pioneering research at the intersection of networking and artificial intelligence.
- Developing innovative methodologies and technical solutions for AI model placement.
- Preparing research papers for submission to prestigious conferences and journals in the fields of networking, AI, and computer science.
- Sharing research outcomes through presentations at academic conferences, seminars, and workshops.
Required Skills
- A robust understanding of networking principles, protocols, and architectures is essential.
- Proficiency in programming languages commonly utilized in AI and networking research.
- Experience with relevant libraries and frameworks is advantageous.
- Capability to design and implement algorithms for complex problem-solving.
- Familiarity with optimization techniques.
- Strong written and verbal communication skills for effectively presenting research findings and collaborating with peers.
- Ability to work collaboratively within a research team, engaging with colleagues from diverse backgrounds.
Benefits
- Subsidized meals.
- Partial reimbursement for public transportation expenses.
- Generous leave policy including annual leave and additional days off.
- Flexible working hours and the option for remote work.
- Access to professional equipment and resources.
- Participation in social, cultural, and sports activities.
- Opportunities for vocational training.
- Comprehensive social security coverage.
