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Contract type: Fixed-term contract
Level of qualifications required: PhD or equivalent
Fonction: Post-Doctoral Research Visit
The Inria centre at Université Côte d'Azur comprises 37 research teams and 8 support services. The centre's staff, approximately 500 people, includes scientists of various nationalities, engineers, technicians, and administrative personnel. The teams are primarily located on the university campuses of Sophia Antipolis and Nice, as well as Montpellier, collaborating closely with research and higher education institutions such as Université Côte d'Azur, CNRS, INRAE, and INSERM, along with regional economic players.
The Inria Centre at Université Côte d'Azur is a significant player in scientific excellence, with research areas including computational neuroscience and biology, data science and modeling, software engineering and certification, and collaborative robotics. The centre is known for its results and collaborations at both European and international levels.
This PostDoc is funded by the Inria-Nokia Bell Labs challenge: LearnNet (Learning Networks).
Researchers involved:
Introduction:
An increasing number of applications rely on complex inference tasks based on machine learning (ML). Currently, two options exist to run such tasks: either served directly by the end device (e.g., smartphones, IoT equipment, smart vehicles) or offloaded to a remote cloud. Both options may be unsatisfactory for many applications: local models may have inadequate accuracy, while the cloud may fail to meet delay constraints. In [SSCN+24], researchers from the Inria NEO and Nokia AIRL teams presented the novel idea of inference delivery networks (IDNs), networks of computing nodes that coordinate to satisfy ML inference requests achieving the best trade-off between latency and accuracy. IDNs bridge the dichotomy between device and cloud execution by integrating inference delivery at the various tiers of the infrastructure continuum (access, edge, regional data center, cloud). Nodes with heterogeneous capabilities can store a set of monolithic machine-learning models with different computational/memory requirements and different accuracy, and inference requests can be forwarded to other nodes if the local answer is not considered accurate enough.
Research goal:
Given an AI model’s placement in an IDN, we will study inference delivery strategies to be implemented at each node. For example, a simple inference delivery strategy is to provide the inference from the local AI model if it seems accurate enough or to forward the input to a more accurate model at a different node if the inference quality improvement (e.g., in terms of accuracy) compensates for the additional delay or resource consumption. Besides this serve-locally-or-forward policy, we will investigate more complex inference delivery strategies, which may allow inferences from models at different clients to be combined. To this purpose, we will rely on ensemble learning approaches [MS22] like bagging [Bre96] or boosting [Sch99], adapting them to IDN distinct characteristics. For example, in an IDN, models may or may not be trained jointly, may be trained on different datasets, and have different architectures, ruling out some ensemble learning techniques. Moreover, queries to remote models incur a cost, which leads to prefer ensemble learning techniques that do not require joint evaluation of all available models.
In an IDN, models could be jointly trained on local datasets using federated learning algorithms [KMA+21]. We will study how the selected inference delivery strategy may require changes to such algorithms to consider the statistical heterogeneity induced by the delivery strategy itself. For example, nodes with more sophisticated models will receive inference requests for difficult samples from nodes with simpler and less accurate models, leading to a change in the data distribution seen at inference with respect to that of the local dataset. Some preliminary results about the training for early-exit networks in this context are in [KSR+24].
Research.
If the selected candidate is interested, they may be involved in students' supervision (master and PhD level) and teaching activities.
Candidates must hold a PhD in Applied Mathematics, Computer Science, or a closely related discipline. Candidates must also show evidence of research productivity (e.g., papers, patents, presentations, etc.) at the highest level.
We prefer candidates with a strong mathematical background (in optimization, statistical learning, or privacy) and a keen interest in using mathematics to model real problems and gain insights. The candidate should also be knowledgeable about machine learning and have good programming skills. Previous experience with PyTorch or TensorFlow is a plus.
Gross Salary: 2788 € per month
Warning: You must enter your e-mail address to save your application to Inria. Applications must be submitted online on the Inria website. Processing of applications sent from other channels is not guaranteed.
Defence Security:
This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST). Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment.
Recruitment Policy:
As part of its diversity policy, all Inria positions are accessible to people with disabilities.
Inria is the French national research institute dedicated to digital science and technology. It employs 2,600 people. Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, often at the interface with other disciplines. The Institute also employs numerous talents in over forty different professions. 900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact.