PhD “Self-supervised anomaly detection and explanation in temporal data” M/F

Job title:

PhD “Self-supervised anomaly detection and explanation in temporal data” M/F

Company:

Orange

Job description

about the roleYour role is to conduct a PhD thesis on: “Self-supervised detection and explanation of anomalies in time series data.”Global Context and Problematic of the Subject
Orange operates a large number of connected devices, from the core network to customers’ LAN. The number of failures and attacks that can occur is significant, and it is becoming increasingly difficult to understand these issues, which incur substantial costs for customer services. So far, state-of-the-art work and research within Orange have focused on detecting anomalies in time series data, particularly in the communications of these devices. This detection results in a binary outcome-anomaly or not-and provides no information about the origin of the detection or any other characterization of the anomaly and/or its potential cause.The explored solutions do not allow for action on detected anomalies, and an expert is still needed to find correlations between anomalies and hypothesize about their origins and corrective procedures.More recently, significant advances in large language models (LLMs) promise interesting perspectives on interpretability issues. With an anomaly characterization model, it would be possible to relate detected anomalies to known issues.1. Najari et al., RADON: Robust Autoencoder for Unsupervised Anomaly Detection, SIN 2021
2. Najari et al., Robust Variational Autoencoders and Normalizing Flows for Unsupervised Network Anomaly Detection, AINA 2022
3. Darban et al., Deep Learning for Time Series Anomaly Detection: A Survey, ACM Computing Surveys 2025Scientific objectives – expected results and challenges
The objective of this PhD is to propose self-supervised deep learning approaches for the detection and characterization of anomalies in time series data. These characteristics should be leveraged to enable the identification of detected anomalies, for example, by utilizing business knowledge databases. The aim is to propose an end-to-end solution for anomaly detection and interpretation without human intervention.Scientific challenges:

• Heterogeneous data and anomalies
• Uncertain presence of anomalies in the data
• Difficult correlation between anomaly characterization and business knowledge
• Potentially unknown anomalies

Expected results

• A robust self-supervised anomaly detection model that characterizes the discrepancies between the observed sequence and the expected normal sequence
• An LLM+RAG-based approach to relate the characterization of detected anomalies with business knowledge
• Experimental evaluations on public and Orange data
• Publication of scientific articles, patents, and the thesis manuscript

about youSkills (scientific and technical)

• You graduated or will graduate from an engineering school and/or a Research Master in computer science and/or applied mathematics.
• You have excellent theoretical and practical knowledge of machine learning and deep learning.
• Experience in the domain of Large Language Models (LLM) and associated techniques (Chain of thought, RAG, GraphRAG, etc.) are greatly appreciated.
• Experience of statistical anomaly detection approaches (Isolation Forest, LOF, etc.) are appreciated.
• Experiences in semantic web, internet of things, connected environments in general are appreciated.
• You have significant experience in Python development, with standard machine learning frameworks (PyTorch, scikit-learn), and code versioning with Git.
• You are methodical, autonomous, curious, and proactive. You work well in a team and share your work both in writing and oral forms.
• You seek to pursue a career in research and innovation.
• You are very comfortable in both French and English (reading, writing, speaking).

Diplomas and educational background

• You graduated or will graduate from an engineering school and/or a Research Master in computer science and/or applied mathematics.
• Research domains: machine learning, deep learning, anomaly detection.

Research experiences

• Research-oriented internship(s) in an academical or industrial setting.
• Experience in publishing scientific articles (journals or conferences) is appreciated.

additional informationThe detection and interpretation of anomalies in data from devices and services is a major challenge for reducing the costs of resolving failures and attacks for Orange’s customer services. The potential application of this thesis work to operational topics is therefore very significant and may lead to internal and external presentations at conferences and professional exhibitions.The scientific approaches to be investigated during this PhD thesis primarily rely on Deep Learning and LLMs. Your work will thus contribute to significant advancements in research on these artificial intelligence techniques and will benefit from high visibility.This PhD thesis offer is collaboratively supervised within Orange by Julien Cumin and Samuel Berlemont, and by a university professor.departmentOrange Innovation brings together the research and innovation activities and expertise of the Group’s entities and countries. We work every day to ensure that Orange is recognized as an innovative operator by its customers and we create value for the Group and the Brand in each of our projects. With 720 researchers, thousands of marketers, developers, designers and data analysts, it is the expertise of our 6,000 employees that fuels this ambition every day.Orange Innovation anticipates technological breakthroughs and supports the Group’s countries and entities in making the best technological choices to meet the needs of our consumer and business customers.Within Orange Innovation, you will be integrated into a research team at the forefront of innovation and expertise in home network services and connected devices. You will be part of a research ecosystem focused on artificial intelligence and digital twins. You will collaborate with other researchers and doctoral students in the field, as well as operational teams working on home network products and services, and connected devices.contractThesis

Expected salary

Location

Meylan, Isère

Job date

Fri, 21 Mar 2025 23:05:07 GMT

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