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Total

Subject : Statistical modelling and uncertainty analysis of battery lifetime.

======Industrial context===

In a global context to reduce the energy consumption and the carbon footprint in order to
fight against the climatic change, a lots of efforts are engaged in particular in transportation with electrification of vehicle and in the energy production with more and more renewable like solar or wind. In this context part of Lithium-Ion batteries in the modern energy management solutions are quickly growing and is becoming one of the critical components in modern electric vehicles or in energy storage solution. However, the cost of such battery remains important in the overall system. In addition the battery performances degrades as long as the battery is used. Indeed, the accurate prediction of how fast the battery will degrade and then how long the battery will be able to be used in the system before having to be replaced is critical. This critical knowledge should avoid to over-size the battery and so is a key advantage for a battery supplier for its competitiveness. Such prediction are adressed today by models which require a lot of intensive tests. In addition the determination of the uncertainty of such model is a key information for battery company to assess the financial risk for commercial bids where they are engaged on the battery lifetime. The goal of the PhD thesis is to develop machine learning methods to estimate in the same way the lifetime of a battery as well as the associated uncertainty.
Saft is a world leader in batteries for lots of different markets and is part of group Total
which has the ambition to become one of the major company in energy. Recently Saft and Total has announced an alliance in particular with PSA to create an European company to adress the volume market of batteries for electrical vehicle in order to help Europe to be competitive and independant against Asian compagnies.

=====Profile=============

The ideal candidate is strongly motivated by environmental questions, passionate about artificial intelligence and engineering, has a solid background in applied mathematics, statistics, and has good scientific writing skills. A proven experience and taste for computer programming and data analysis is required.
Candidates should hold a MSc in Computer Science, Applied Mathematics, Engineering or
related fields. A strong command of English language is also required.

=====Application===========
Application files must be sent to : sebastien.benjamin@saftbatteries.com, antoine.bertoncello@total.com and marianne.clausel@univ-lorraine.fr before October 31 and must include :
— A cover letter or email,
— A CV, including contact information for two or more referees
— A research outcome (Master’s thesis or paper) written by the candidate
— A transcript of grades
Incomplete application files will not be considered.

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SCAI / LTU

Knowledge Graph Embedding & GAN for Medieval Manuscript Studies


We will develop innovative AI techniques to “augment” the historical content of texts and images to study cultural meanings and representations of music and sound in the Middle Ages. Our aim is to develop techniques to both (i) recognize or at least detect textual elements related to performance (words, phrases, etc.) in texts between the 5th and 15th century, and (ii) identify performances (musicians, instruments) in images. The first challenge is to incorporate different data sources lacking explicit contextual linking. Because of the complexity of the subject — the variety of sources, languages,
and artistic contexts — we would like to continue implementing more complete transcultural and diachronic knowledge graphs. Since, knowledge graph embedding techniques have not been exploited in the medieval musicology domain; we will explore different approaches using purely knowledge graph embedding, visual embedding or combined embedding to calculate the similarity between items. Another major challenge in Deep Learning is the need for vast amounts of labeled data for training the images. In
this project, we will use Generative Adversarial Networks (GAN) and Neural Network Style Transfer (NNST) for the generation of large training databases and introduce a pioneering approach towards the generation and completion of historical images containing musical instruments. Furthermore, we will introduce the idea of using GAN for domain transfer between these two domains, i.e., historical representations to “real-life” representations and vice versa. This will be useful for data augmentation as well as image enhancement and completion. Natural Language Processing (NLP) techniques will extract references to musical performances in texts: multilingual for medieval languages and multiple versions of
descriptions are a novel challenge.

Keywords

Knowledge graphs, Ontologies, Cultural Heritage, IIIF, Knowledge graph embedding, Graph Neural Networks, Generative Adversarial Networks (GAN), Neural Network Style Transfer (NNST).

Contact

Dr Xavier Fresquet
Deputy Director of SCAI
phone: + 33 1 44 27 76 05
email: xavier.fresquet@sorbonne-universite.fr
4, Place Jussieu
75005 Paris