Post-doc

AI REGULATION POST-DOCTORAL CANDIDATE

Description

The story so far: We are seeking a highly motivated and experienced post-doctoral researcher to join our team
as an AI Regulation Legal Researcher. This unique role is a part of our ‘AI Law and Governance in a Global
Economy’ research chair, a collaborative Chair between ANITI, one of the recently appointed French AI
Clusters, and the Canadian Research Chair on International and Comparative AI Law at the University of Ottawa
(Canada), in partnership with Airbus (industrial partner).
The successful candidate will be immersed in a dynamic team of legal counsels within AIRBUS company’s legal
department, where they will be expected to provide valuable insights from a top-down approach, translating
theoretical research into practical, real-world applications. Additionally, they will employ a bottom-up
approach, learning from field experiences and ground-level issues to challenge and improve the theoretical
aspects of AI regulation.

The context: You will join a team of legal researchers (University of Toulouse & University of Ottawa) and
industrial legal counsels (Airbus). Your time will be divided between on-site collaboration at Airbus at the legal
department and within the research lab in Toulouse (and in Ottawa as an option), including some remote work
if you wish. Your time will be dedicated to the research Chair and will be supervised both by a legal academic
researcher and a legal industrial researcher.

Missions

● Support the development of a comprehensive AI compliance and governance program: This project
aims to create a robust AI compliance program that meets high ethical and legal standards, including
addressing bias, ensuring fairness, and maintaining accountability. It will cover all aspects of AI
development, deployment, and use, integrating various branches of law in the context of AIrbus.

● Analyze comparative AI regulations: To ensure a well-rounded compliance program for a
multinational company as Airbus, we will conduct a comparative law analysis of AI regulations from
various countries. This includes analyzing public policies, emerging legislation, and doctrinal studies,
as well as identifying norms and their enforcement.

● Evaluate the AI compliance program from a research and empirical standpoint: To test the program’s effectiveness, we will subject it to empirical challenges and real-world use cases within the company and its ecosystem. The goal is to identify strengths and weaknesses, generate practical guidelines for businesses, and create a model program that can serve as a blueprint for other organizations.

● Extrapolate broader digital compliance and governance research: This project also aims to extrapolate broader digital regulations from the AI compliance program. The goal is to develop technology-neutral digital compliance and governance programs that can adapt to future technologies and regulations. This will pave the way for ethical and legal digital use in major companies and enrich governance models and law.

● Artificial Intelligence (AI) and robotics Law: Addressing legal and ethical considerations associated with AI development, deployment, and the use of robotics. Ensuring the implementation of the AI Act and other relevant AI regulations at Airbus.

Profile and required skills

● Academic background: A strong EU digital law academic background, with a focus on artificial intelligence. Candidates must have a PhD in law or equivalent, with a proven track-record of academic excellence and research expertise in the relevant field.

● Experience: a few years of professional experience in digital law both in a law firm and/or as regulatory consultant or in-house counsel would be ideal.

● Digital Knowledge: Profound understanding of a wide array of information technology and digital domains, including cloud, artificial intelligence technologies such as machine learning, neural networks, generative AI, and natural language processing.

● Problem-Solving: Strong analytical and problem-solving abilities, with a proactive and solutionoriented approach. Risk management capabilities are a must-have.

● Collaborative Team Player: The ability to collaborate effectively with cross-functional teams at all levels of the organization.

● Ethical and Trustworthy: A commitment to upholding the highest ethical standards in AI development/deployment, and in digital regulation.

● Adaptability: Ability to work with a high diversity of stakeholders (e.g., various business functions, authorities, academics, law firms, other companies), with innovative approaches.

● Language: The successful candidate will be fluent in English. German and/or French would be an asset.

Duration: 12 months (couId be extended) starting preferably September 2024

Salary: according to experience and profile

Location: France, Toulouse area

Advisor: Pr. Céline Castets-Renard ( Ottawa U) & Dr. Benjamin La roche (Airbus)

Postdoc position at Météo-France (CNRM) in Artificial Intelligence for Numerical Weather Prediction

This position is part of the Chair EXPLEARTH, endorsed by the ANITI Institute (https://aniti.univ-toulouse.fr/). The main objective of EXPLEARTH is to develop a new generation of weather prediction systems, based on hybridisation of traditional physical models and state-of-the art ML methods, allowing for increased accuracy and timeliness in a cost-effective way.

Objectives
Currently operational weather forecasts rely on physically-based modelling approaches, and Numerical Weather Prediction (NWP) models are operated to determine atmospheric conditions for the next hours and days. The configuration choices of NWP models are still strongly constrained by computational resources, which implies in particular limitations on the horizontal resolution. Current operational systems run with a resolution around 10 km at the global scale, and around 1 km at the regional scale, at best.
A cheaper alternative to the explicit increase of the computational grid (also known as dynamical downscaling) is statistical downscaling, which aims at learning a relationship between coarse-scale and finer-scale forecasts. This downscaling task is very similar to super resolution in computer vision.
The aim of the position is to develop and evaluate state-of-the-art ML methods for statiscal downscaling applied to the Arome forecasting model operational at Météo-France.

Required skills

The ideal candidate would have the following qualifications :
– A PhD degree in atmospheric sciences, statistics or artificial intelligence
– A strong background in deep learning algorithms, in particular convolutional neural networks and deep generative models (GANs, diffusion models)
– Experience in geophysical problems would be appreciated, at least a strong interest for applied research in atmopsheric physics is highly recommended
– Proficiency with Python programming and AI librairies (tensorflow, PyTorch)
– Experience with processing large volumes of data
– Experience of working in a Linux-based environment
– Aptitude for scientific work, written and oral communication in English, meetings abroad possible
– A scientific curiosity, autonomy, rigor in the interpretation of the results

Practical aspects

This work will be carried on at the National Centre for Meteorological Research (CNRM), in Toulouse, France. The EXPLEARTH project includes partnerships with CERFACS, EVIDEN, and the Toulouse Institute of Mathematics, among others. The candidate will work in close collaboration with the partners, and will contribute to ANITI activities.
The successful candidate will benefit from the Meteo-France/CNRM computational facilities and will have access to the forecasts datasets of Météo-France.
The net monthly salary will be between 2400 and 3700 euros according to experience. This includes French social security (health insurance).

Position

Application deadline: 12 July 2024
Duration of contract : 24 months
Expected starting date : October 2024
Location : Toulouse, France
Department : Research

Postdoctoral researcher position for Audio Mobility 2030

This post-doctoral position on trustworthy language models for  AI is proposed in the framework of the Audio Mobility 2030 (AM2030) project, which started in April 2023. AM2030 aims at enabling car manufacturers to have their own in-car audio applications, regardless of the operating system. They will be able to deploy a global audio experience and offer the best content and proactive services to drivers. It is positioned as a true road companion that will help consumers adopt eco-responsible behaviors: vehicle self-diagnosis and maintenance reports, advice on driving, and the use of on-board equipment.

Project partners: ETX Studio (Lead), Continental Automotive FRANCE SAS, Université de Toulouse – ANITI, École Polytechnique de Paris.

ANITI’s role in the project is related to working on human-computer interactions, in particular on natural language understanding.  This will include a conversational model that can exploit conversational structure and can reliably acquire semantic content as well as content provided by modern language models.  The model will learn constraints on the user’s preferences, from the conversation and from his previous choices.

The conversational assistant will go considerably beyond the art of current finite state dialogue systems but offering transparency guarantees and explainability that large transformer models by themselves do not offer.  It will interact with voice-based components as well as a recommendation model for actions based on the information acquired by the conversational assistant.

Required skills

Applicants should have a PhD in computer science or a related area. Applicants should also have working knowledge of machine learning and familiarity with language models. Good programming skills in python are essential as is an ability to interact with a large research team; English communication skills are also required

Contract : post-doc

Duration : 24 months

Salary : according to experience

Advisor : Farah Benamara & Nicholas Asher

Postdoctoral researcher position for Audio Mobility 2030

This post-doctoral position on explainable AI is proposed in the framework of the Audio Mobility 2030 (AM2030) project, which started in April 2023. AM2030 aims at enabling car manufacturers to have their own in-car audio applications, regardless of the operating system. They will be able to deploy a global audio experience and offer the best content and proactive services to drivers. It is positioned as a true road companion that will help consumers adopt eco-responsible behaviors: vehicle self-diagnosis and maintenance reports, advice on driving, and the use of on-board equipment.

Project partners: ETX Studio (Lead), Continental Automotive FRANCE SAS, Université de Toulouse – ANITI, École Polytechnique de Paris.

ANITI’s role in the project is related to working on human-computer interactions, in particular on natural language understanding.  This will include a conversational model that can exploit conversational structure as well as content provided by modern transformer-based models.  The model will learn constraints on the user’s preferences, from the conversation and from his previous choices.

The conversational assistant will go considerably beyond the art of current finite state dialogue systems but offering transparency guarantees and explainability that large transformer models by themselves do not offer.  It will interact with voice-based components as well as a recommendation model for actions based on the information acquired by the conversational assistant.

Required skills

Applicants should have a PhD in computer science or a related area. Applicants should also have working knowledge of machine learning. Good programming skills are essential; English communication skills are also required.

Contract : post-doc

Duration : 12 months

Salary : according to experience

Advisor : Joao Marques Silva & Nicholas Asher

Postdoctoral position on conversational agents for Audio Mobility 2030

This posdotoral position on conversational agents is proposed in the framework of the Audio Mobility 2030 (AM2030) project, which started in April 2023. AM2030 aims at enabling car manufacturers to have their own in-car audio application, regardless of the operating system. They will be able to deploy a global audio experience and offer the best content and proactive services to drivers. It is positioned as a true road companion that will help consumers adopt eco-responsible behaviors: vehicle self-diagnosis and maintenance reports, advice on driving and the use of on-board equipment.

Project partners: ETX Studio (Lead), Continental Automotive FRANCE SAS, Université de Toulouse – ANITI, École Polytechnique de Paris

ANITI’s role in the project is related to working on human-computer interactions, in particular on natural language understanding.  This will include a conversational model that can exploit conversational structure as well as content provided by modern transformer based models.  The model will learn constraints on the user’s preferences, from the conversation and from his previous choices.

The conversational assistant will go considerably beyond the art of current finite state dialogue systems but offering a transparency, guarantees and explainability that large transformer models by themselves cannot.  It will interact with voice based components as well as a recommendation model for actions based on the information acquired by the conversational assistant.

Required skills

Applicants should ideally have a PhD in machine learning. Good programming skills is essential; and English communication skills are also required.

Contract : post-doc

Duration : 24 months

Salary : according to experience

Advisor : Nicholas Asher

Automatic speech recognition for an in-car voice assistant

This PostDoc position is proposed in the framework of the Audio Mobility 2030 (AM2030) project, which started in April 2023. AM2030 aims at enabling car manufacturers to have their own in-car audio application, regardless of the operating system. They will be able to deploy a global audio experience and offer the best content and proactive services to drivers. It is positioned as a true road companion that will help consumers adopt eco-responsible behaviors: vehicle self-diagnosis and maintenance reports, advice on driving and the use of on-board equipment.

Project partners: ETX Studio (Lead), Continental Automotive FRANCE SAS, ANITI, Université de Toulouse, École Polytechnique de Paris.

ANITI’s role in the project is related to working on human-computer interactions, in particular on natural language understanding. The role of the hired PostDoc researcher will be to work more specifically on automatic speech (ASR, Speech-To-Text) in a noisy environment (the interior of a car). Two lines of research are envisaged: 1) adapting state-of-the-art open-source ASR models and self-supervised speech representation models (Wav2Vec2) to the noisy context of vehicles
(presence of music/radio in the background, engine noise, wind noise, rain, etc.), 2) working on the language models that constrain end2end systems. Depending on the candidate research profile, one of these research lines will be chosen,

This research will be conducted in connection
with the two other aspects treated by ANITI: 1) the study of the conversational structures between the driver and the assistant and their semantic interpretation, 2) the detection of
emotions and states of mind based on speech and transcription cues.

The hired PostDoc will be based at the Computer Science Research Institute of Toulouse (IRIT, located in the campus of the Toulouse III Paul Sabatier University. They
will be integrated in the Samova team, composed of about twenty permanent staff, PhD students and PostDocs whose research is related to various aspects of AI applied to speech and audio processing (https://www.irit.fr/SAMOVA/site/).

Required skills

Applicants should have a PhD in machine learning, ideally in speech/natural language processing.
Good programming and English communication skills are also required.

References

• Baevski, A., Zhou, H., Mohamed, A., and Auli, M. wav2vec 2.0: A framework for self-supervised
  learning of speech representations. arXiv preprint arXiv:2006.11477, 2020
  
• Radford, A., Kim, J. W., Xu, T., Brockman, G., McLeavey, C., & Sutskever, I. (2022). Robust speech
  recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356
  
• L. Gelin, M. Daniel, J. Pinquier, T. Pellegrini, 2021. End-to-end acoustic modelling for phone
  recognition of young readers. Speech Communication, 134, pp. 71-84.

Contract : post-doc

Duration : 24 months

Salary : according to experience

Location : Computer Science Research Institute of Toulouse (IRIT), Toulouse, France

Advisor : Thomas Pellegrini

Conversational sentiment analysis for an in-car voice assistant

This PostDoc position is proposed in the framework of the Audio Mobility 2030 (AM2030) project, which started in April 2023. AM2030 aims at enabling car manufacturers to have their own in-car audio application, regardless of the operating system. They will be able to deploy a global audio experience and offer the best content and proactive services to drivers. It is positioned as a true road companion that will help consumers adopt eco-responsible behaviors: vehicle self-diagnosis and maintenance reports, advice on driving and the use of on-board equipment.

Project partners: ETX Studio (Lead), Continental Automotive FRANCE SAS, ANITI, École Polytechnique de Paris.

ANITI’s role in the project is related to working on human-computer interactions, in particular on natural language understanding. The role of the hired PostDoc researcher will be to develop a Conversational Sentiment Analysis system that will be able to detect the polarity and emotion of speakers based on an ongoing interaction. Two main tasks are planned:

1. Analysis of the discursive structure of the conversation to identify speaker’s
   needs/goals/preferences.
   
2. Exploit this structure as well as past conversations to build the subjective profile of the
   speaker via the detection of the conveyed sentiments (positive vs. negative) as well as the
   emotional states of the speaker. Potential malevolent dialogues (e.g. aggressivity) will also
   be detected.
   
3. Inject this subjective profile to the recommender system to increase its performances.
   The hired PostDoc will be based at the Computer Science Research Institute of Toulouse (IRIT,
   https://www.irit.fr/en/), located in the campus of the Toulouse III Paul Sabatier University. They will be integrated in the Melodi team, composed of about 30 permanent staff, PhD students and PostDocs whose research is related to various aspects of AI applied to text and dialogue processing (https://www.irit.fr/departement/intelligence-artificielle/equipe-melodi/).

Required skills

Applicants should have a PhD in machine learning, ideally in speech/natural language processing.
Good programming and English communication skills are also required.

References

• Patricia Chiril, Endang Wahyu Pamungkas, Farah Benamara, Véronique Moriceau, Viviana Patti: Emotionally Informed Hate Speech Detection: A Multi-target Perspective. Cogn. Comput. 14(1): 322-352 (2022)
  
• Farah Benamara, Maite Taboada, Yvette Yannick Mathieu. Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications. Comput. Linguistics 43(1): 201-264 (2017)
  
• Farah Benamara, Nicholas Asher, Yvette Yannick Mathieu, Vladimir Popescu, Baptiste Chardon: Evaluation in Discourse: a Corpus-Based Study. Dialogue Discourse 7(1): 1-49 (2016)

Contract : post-doc

Duration : 24 months

Salary : according to experience

Location : Computer Science Research Institute of Toulouse (IRIT), Toulouse, France

Advisor : Farah Benamara

Constraint and preference acquisition for an interactive drive assistant

A postdoc position is offered within the CORAM project Audiomobility 2030, which is centered around a plan to develop an interactive assistant for drivers that will interact via voice and also visuals to help instruct and entertain passengers and driver during a car trip.

The research will be focused on acquiring a constraint model of the driver’s preferences integrating the symbolic constraints gleaned from the conversational interactions provided by another component of Audio Mobility 2030, the conversational assistant.

Project partners: ETX Studio (Lead), Continental Automotive FRANCE SAS, ANITI, École
Polytechnique de Paris

The post doc will be based at LIRMM, Montpellier, France.1
The goal of this postdoc is to design a method to acquire a constraint model representing implicit information about the driver’s (and passengers’) preferences.

This acquisition process will be based on what we know about driver/passengers (identity,
preferences, history), as well as the context (alone/accompanied, weather, short/long trip,
work/holiday…).

The proposed method may be based on prior work on the topic of constraint acquisition [Bessiere
et al. 2017]. Modeling this information by a constraint network allows the system to validate the
1 Part of the work can be done at LAAS-CNRS, Toulouse, France, depending on the recruitee’s
wishes.

Required skills

Applicants should have a PhD in artificial intelligence, ideally in constraint programming, Boolean
Satisfiability, and/or Machine Learning. Good programming and English communication skills are
also required.

References:

• Christian Bessiere, Frédéric Koriche, Nadjib Lazaar, and Barry O’Sullivan. Constraint acquisition. Artificial Intelligence, 244:315–342, 2017. Combining Constraint Solving with Mining and Learning

Contract : post-doc

Duration : 24 months

Salary : according to experience

Location : Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier LIRMM

Advisor : Christian Bessiere et Emmanuel Hebrard