CDD, 19 Place Marguerite Perey, 91120 Palaiseau, France.| Entreprise/Organisme : | Télécom Paris | | Niveau d'études : | Doctorat | | Date de début : | 1 janvier 2027 | | Durée du contrat : | 3 ans | | Secteur d'activité : | Enseignement-recherche en apprentissage Statistique et informatique quantique | | Description : | Important Dates
• October 31, 2026: Application deadline
• November 18, 2026: Selected candidates are due to audition
Telecom Paris’s [1] machine learning, statistics and signal processing group (a.k.a S²A group) [2], within the laboratoire de traitement et communication de l’information (LTCI) [3] and the Image, Data & Signal (IDS) department [4], is inviting applications for a 3 years (renewable once) faculty position at the Associate Professor level (Maitre de Conferences) in ‘Quantum Machine Learning’. The new hire will also benefit from the support and complementary expertise of the Quriosity team [5] in the field of quantum computing and quantum learning theory and will be expected to foster collaboration between the two teams.
[1] https://www.telecom-paris.fr/en/home
[2] https://s2a.telecom-paris.fr
[3] https://www.telecom-paris.fr/en/research/labs/information-processing-ltci
[4] https://www.telecom-paris.fr/en/school/departments/image-data-signal
[5] https://quriosity.telecom-paris.fr/
Additional information Hi! PARIS
This recruitment is made possible thanks to Hi!Paris [6], the interdisciplinary center on Data Analytics and AI for Science, Business and Society created by Institut Polytechnique de Paris (IP Paris) and HEC Paris and joined by Inria (Centre Inria de Saclay) and the financial support of France 2030 IA Cluster. It has been backed by strong funding and support from major multinational companies. On a larger scale than ever before, Hi!Paris is conducting groundbreaking and interdisciplinary research in AI and Data Science, shaping strategic insights for economic and public decision-makers in France and Europe. In 2024, the Center was officially designated as an “AI Cluster” by the French government.The center is home for leading researchers in AI & Data Science, it offers seminars, workshops, summer schools, reading groups, fundings through calls for project and the valuable support of an engineering team.
The person recruited will benefit from a starting grant to finance his or her research activities.
[6] https://www.hi-paris.fr
Main missions
The person recruited must have the experience and expertise required to carry out the following research and teaching assignments. The new hire is not necessarily a researcher who is already an expert in the field, but rather an expert in statistical learning who is motivated by the scientific challenges posed by its development in the context of recent advances in quantum computing.
Skills
Main expertise : (Quantum) Machine-learning (theory and algorithms)
Other expertise desired: Statistics in high dimension, Simulation, Stochastic approximation/optimization, Structured data (e.g. graphs, functional data)
Research activities
Develop groundbreaking research in the field of quantum machine learning, aligned with the topics of the S²A group ofthe Images, Data & Signals department and those of the Quriosity team of the Computer Science & Networks department. The researcher hired will pursue interdisciplinary research in statistical learning (theory and algorithms) in connection with the methodological issues raised by quantum computing. The prospect of harnessing the properties of quantum superposition and entanglement via quantum computers has recently given rise to the field of quantum statistical learning (learning for quantum information processing and quantum computing for learning), which aims to design new types of algorithms and establish a solid theoretical foundation for them, and is undergoing rapid development. The enhanced expressivity of quantum circuits with respect to their classical counterpart, together with the distinctive features of quantum information tasks and resources, open the door to new paradigms for learning. The development of quantum AI requires the creation of new analytical approaches, theoretical concepts, and algorithmic methods. Specifically, this involves:
➔ designing statistical inference/learning techniques suited to the analysis of quantum information (e.g., structured/functional data, graph models)
➔ developing learning algorithms that leverage the properties of quantum physics (e.g., qubits) for scaling
Develop both academic and industrial collaborations on the same topic, including collaborative activities with other Telecom Paris research departments and teams, and research contracts with industrial players.
Set up research grants and take part in national and international collaborative research projects.
Teaching activities
• Participate in teaching activities at Telecom Paris and its partner academic institutions (as part of joint Master programs, e.g. https://quriosity.telecom-paris.fr/qmi/qmi.html, https://www.ip-paris.fr/en/education/masters/applied-mathematics-and-statistics-program/master-year-2-data-science ), especially in machine learning, data science and quantum computing, including life-long training programs.
Impact
• Publish high quality research work in leading journals and conferences.
• Be an active member of the research community (serving in scientific committees and boards, organizing seminars, workshops, special sessions…).
Candidate profile
As a minimum requirement, the successful candidate will have:
• A PhD degree
• A track record of research and publication in the field of machine learning
• Experience in teaching
• Good command of English
The ideal candidate will also (optionally) have:
Some understanding of the challenges of quantum computing for machine learning—and vice versa
A post-doctoral or international experience in an academic or industrial laboratory
NOTE:
The candidate does *not* need to speak French to apply, just to be willing to learn the language (teaching will be mostly given in English)
Other skills expected include:
• Capacity to work in a team and develop good relationships with colleagues and peers
• Good writing and pedagogical skills
More about the position
• Place of work: 19 place Marguerite Perey, 91120 Palaiseau, France. You’ll be working in a fast-growing, pleasant, green and accessible environment (particularly for people with disabilities) just 20 km from Paris (RER B and C, close to major roads, shared shuttle service from Porte d'Orléans).
49 days' annual leave (CA + RTT)
Flexible working hours (depending on the department's activity)
Teleworking 1 to 3 days/week possible
75% reimbursement of public transport season ticket
Proximity to numerous sports facilities, concierge service, underground car park, in-house catering, etc.
Staff association at school and department level
Good to know: our social security contributions are lower than in the private sector
How to apply
Applications are sent using the link
https://institutminestelecom.recruitee.com/l/en/o/assistantassociate-professor-in-quantum-machine-learning-tenure-track
https://institutminestelecom.recruitee.com/o/assistantassociate-professor-in-quantum-machine-learning-tenure-track
The application should include:
• A complete and detailed curriculum vitae
• A letter of motivation
• A document detailing past activities of the candidate in teaching and research: the two types of activities will be described with the same level of detail and rigor.
• The texts of the 3 main publications and a publication list
• The names and addresses of two referees
• A short teaching project and a research project (maximum 4 pages for each)
Please, do not hesitate to contact the team before applying.
Contacts :
Stephan Clémençon (head of the S²A group)
stephan.clemencon@telecom-paris.fr
Florence Tupin (head of the IDS department)
florence.tupin@telecom-paris.fr
Marco Fanizza (researcher at INRIA Quriosity)
marco.fanizza@inria.fr | | En savoir plus : | https://institutminestelecom.recruitee.com/o/assistantassociate-professor-in-quantum-machine-learnin
ChaireExt_S2A_QML.pdf | | Contact : | stephan.clemencon@telecom-paris.fr |
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