University Grenoble Alpes and Savoye offer a PhD position on time time series prediction (see description below), starting ideally in September 2023.
The successful candidate will work both at Savoye, in the Advanced Research and Innovation team, and Univ. Grenoble Alpes, in the Aptikal team. She/he will also be a member of the Grenoble Interdisciplinary Institute in Artificial Intelligence (
https://miai.univ-grenoble-alpes.fr/).
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PhD description: Savoye is a fast-growing mid-sized company with around 1,000 employees that offers hardware and software solutions to automatize warehouses and enhance logistics processes. One of our flagship solution pertains to
high-density robotic storage systems that Savoye pioneered (https://https://www.savoye.com/us/ressource/
x-pts-shuttles/). Savoye is expanding quickly, thanks in particular to its expansion into the American and Asian
markets (revenue growth >20%/year). Our ambition is to become one of the main players in the intralogistics sector
worldwide in the next few years. In 2022, Savoye introduced machine learning for the first time in one of its products as part of a workforce
management feature. In order to continue the efforts undertaken in the field of time series prediction, we propose a
CIFRE whose purpose is to explore and exploit state-of-the-art techniques. Several topics and strategic issues will
support this research :
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— The prediction of flows and article demands, for example, is at the heart of our concerns to optimize
inventory management by forecasting customers needs,
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— The estimation of the time required to carry out logistics operations (ETA, Estimated Time of Arrival)
is a major challenge that can allow for a significant improvement in the management of the logistics
activities in the warehouse (taking into account the effect of routing decisions on ETA),
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— Forecasting of failures and break-down of equipment is also a critical topic with a view to expanding
our maintenance service offering.
To address these issues, we will draw on recent developments in artificial intelligence on deep neural
networks and causality. In particular, we want to explore transformer-based models ([5]) for flow and demand
prediction and time estimation, and discovery and causal inference models for fault prediction ([1]). One of the major problems lies in the fact that these models have excellent performance on data close
to those used for their training and often poor performance on data that differs from those used in training. We intend to solve this problem by using generic causal representations ([6]) because they are representative of the
underlying physical phenomena. The causal graph and associated latent variables can be partly extracted from the
available data from discovery and causal inference methods ([1]). This will improve the genericity of models and
their transferability to different contexts. In addition, the available data are often partial, with missing values, and are based on different trends
that are repeated at more or less regular intervals. We introduced the notion of pseudo-periods to designate the
fact that repetitions are not entirely regular and proposed a mechanism capable of identifying them with recurrent neural networks as LSTMs ([2]). This thesis will also be an opportunity to explore, on the one hand, new
methods for the identification of trends and pseudo-periods to improve predictions and, on the other hand, new
methods to optimize the use of partial or missing data (e.g. data produced during lockdowns).
It should be understood that these topics/themes are neither fixed nor exhaustive but constitute a basis
for discussion and work.
Savoye Advanced Research and Innovation Service: The PhD student will be part of the Advanced Research and Innovation team, which reports to the group’s
top executives. Its scope is to explore the state of the art of research to identify and explore (by prototyping or
carrying out proofs of concept) solutions that may ultimately significantly improve the company’s products or
propose radically new solutions. She/he will work within a team specialized in AI in the broad sense (from operations research and mathematical
optimization to different ML techniques) in direct collaboration with the AI/ML tech lead and will aim to extend
the already in-depth research that has been conducted on the subject. Amazon’s research illustrates (but does not guide) our trajectory from a technological perspective ([4]).
Experiments based on models focused on attention mechanisms (transformers) are at the heart of our current
research activities. We are also interested in exploring innovative approaches like DeepMind for Google Maps ([3]).
University Grenoble Alpes: The person recruited will be integrated into the APTIKAL team of the Grenoble Computer Science
Laboratory and will be co-supervised by Émilie Devijver (CNRS researcher, mathematician) and Éric Gaussier
(professor of computer science at the University of Grenoble Alpes, Director of MIAI - Multidisciplinary Institute
in Artificial Intelligence). The APTIKAL team is specialized in machine learning and knowledge acquisition and
in particular on statistical learning and causality, with both broad applications and more targeted applications on
natural language processing or information retrieval for example.
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-- Eric Gaussier
Grenoble University, LIG Laboratory
