Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LIP6
Durée : 4 to 6 months
Contact : lionel.tabourier@lip6.fr
Date limite de publication : 2024-03-31

Contexte :
Understanding the impact of internet anomalous events at internet scale, such as performance degradation, outages, or attacks, is a challenging problem. If techniques and systems have been designed to detect outages at some particular internet facilities, or detect congestion between interdomain links, there exists no internet scale system to monitor events across all autonomous systems (ASes), and thus, we have no clear understanding on the impact of an event on the internet.

The BGP protocol allows ASes to interconnect, so that each AS can reach the prefixes containing the IP addresses of another AS via the routes received with BGP. As most internet events rarely last more than tens of minutes, to capture them, we need to run traceroutes towards each BGP prefix announced by all the ASes very frequently. And in addition to these background measurements, we need to be able to run even more targeted measurements during an event, in order to have a precise understanding of the behavior of the internet paths before and after this event.

Unfortunately, public measurement systems, such as RIPE Atlas and CAIDA Ark do not offer such measurements or the possiblity to run them. They either perform meshed traceroutes between hundreds of sources and destinations at short intervals (15 minutes), or perform traceroutes to one destination per BGP prefix from a hundred of vantage points every day. This is neither sufficient to have an internet scale coverage nor to cover most internet events.

Sujet :
We propose to design this missing measurement system, that will run background traceroutes at high speed every 15 minutes from a few vantage points to one destination in each BGP prefix announced by any AS. When an event is detected, we will run targeted measurements using
propagation algorithms to understand how this event spreads on the internet.

Profil du candidat :
This internship is directed at Master students (preferably Master 2 students) with a background in computer science. Good coding skills are requested for the internship, knowledge of a widely-used language in learning, such as python, is preferable but not mandatory.

Formation et compétences requises :
Background in computer networking, system building, and graph theory are at the heart of the internship, so a background in those areas is an asset, but not mandatory.

Adresse d’emploi :
LIP6, Sorbonne University (4 place Jussieu, 75005, Paris)

Document attaché : 202312201034_Measuring_Event_Impact.pdf

Offre en lien avec l’Action/le Réseau : RoCED/– — –

Laboratoire/Entreprise : INRAE Toulouse
Durée : 6 mois
Contact : mathieu.fauvel@inrae.fr
Date limite de publication : 2024-02-28

Contexte :
Over the last ten years, Earth Observation (EO) has made enormous advances in terms of spatial and temporal resolutions, data
availability and open policies for end-users. The increasing availability of complementary imaging sensors allows land ecosystems
state variables and processes to be observed at different spatio-temporal scales. Big EO data can thus enable the design of new
land monitoring systems providing critical information in order to guide climate change monitoring, mitigation and adaptation.
Conventional machine learning methods are not well adapted to the complexity of multi-modal, multi-resolution satellite image
time series (SITS) with irregular sampling. Therefore they are not suitable for extracting and processing all the relevant infor-
mation. On the other hand, methods based on deep neural networks have shown to be very effective to learn low-dimensional
representations of complex data for several tasks and come with high potential for EO data. However, they often emerge from the
computer vision (CV) and natural language processing (NLP) communities and need to be extended and properly instantiated to
handle the very specificities of Earth Observation data.
Previous works at the CESBIO-lab have shown that generative encoder-decoder architectures such as the Variational Auto-
Encoder (VAE) or the U-NET models perform very well for a variety of EO tasks : estimation of biophysical parameters or
Sentinel-1 to Sentinel-2 translations, to cite a few.
However, such approaches appear to be inadequate to handle data coming from more than 2 sources and acquired at different
time and spatial resolutions, as prioritized in the RELEO chair within ANITI. In particular, the generative capability of these
models may generalize poorly to unseen regions or temporal periods. Processing such streams of data requires to jointly encode
all sources into a structured latent space where each complementary information carried by each source can be embedded while
ensuring long-term encoding of newly acquired data (from possibly new sensors).

Sujet :
The objective of this internship is to investigate Gaussian process (GP) prior for Variational Auto-Encoders (VAEs). Usually,
VAEs assume independence between samples. This assumption is generally made for sake of simplicity and computational ef-
ficiency of the training and inference steps. However, assuming independence of samples amount to ignoring the correlation
between adjacent pixels in the temporal domains. Furthermore, because of the very deterministic nature of such neural networks
architectures, they do not properly encode uncertainty related to missing/noisy data.
Adopting this GP prior is expected to model correlations between times. However, due to the irregular and unaligned nature of
SITS and their massive volume, approximation are required to maintain fast training and inference.
The work-plan of this Master internship is as follows :
1. Define GP prior VAEs for pixel satellite time series with different approximations,
2. Implement the models in PyTorch,
3. Perform experiments on massive SITS and compare with others VAEs on downstream tasks.

Profil du candidat :
Master or Engineering school students with major in applied mathematics, computer science or electrical engineering.

Formation et compétences requises :
The knowledge needed for this work includes a strong background in machine learning or data science, signal & image
processing or remote sensing data processing. Good scientific programming skills (e.g., Python) and good communication skills
in English, both written and oral are also expected. Interests in Earth observation will be appreciated.

Adresse d’emploi :
Centre d’Etudes Spatiales de la Biosph`ere (CESBIO) & INRAE

Document attaché : 202312190628_proposal_MSc_2024_ANITI.pdf

Offre en lien avec l’Action/le Réseau : DSChem/– — –

Laboratoire/Entreprise : Groupe de recherche en informatique, image, automa
Durée : 6 mois
Contact : jean-luc.lamotte@unicaen.fr
Date limite de publication : 2024-02-28

Contexte :
Ce stage prend place dans une collaboration de longue date entre trois laboratoires :
1. Le GREYC (UMR 6072, Caen), a développé au cours des années une expertise forte en fouille de données et apprentissage appliqué à la Chemoinformatique,
2. Le CERMN (UR 4258, Caen) est le centre d’études et de recherche sur le médicament de Normandie et entretient depuis de nombreuses années une collaboration avec le GREYC sur l’analyse informatique de molécules pour créer de nouveaux médicaments,
3. Le LITIS (UR 4108, Rouen), a acquis une solide expérience en apprentissage machine qu’il applique notamment sur des bases de graphes.
Le GREYC et le LITIS collaborent activement au sein de la fédération NormaSTIC.

Des membres des trois laboratoires participeront à l’encadrement du stage en y apportant leurs compétences respectives.

Ce stage peut être considéré comme une étape préparatoire à une thèse de doctorat sur le même sujet.

Sujet :
Le stage commencera par une étude des méthodes GNN permettant de prédire les propriétés de nos jeux de données. L’étude sera ciblée sur la prédiction des interactions protéines/ligands à partir des structures des molécules. A
cette occasion, des méthodes basées GCN, au sens large, et GCN+pooling seront étudiées.

Si cette étape est validée, nous aborderons une comparaison des méthodes de la littérature permettant d’expliquer les résultats de ces GNNs. Les résultats produits (en termes d’explication) seront évalués en utilisant plusieurs critères tels que l’accuracy, l’aire sous la courbe, la fidélité, la parcimonie,. . .. Nous espérons identifier à partir de cette étude des sous structures pharmacophoriques pertinentes pour les propriétés à prédire.

Nous essaierons, dans un troisième temps, d’appliquer ces méthodes sur les graphes moléculaires squelettiques. Il s’agira de comparer sous l’angle de l’explicabilité les descriptions moléculaires ”brutes” et celles produites en intégrant une expertise du domaine via le graphe pharmacophorique.

Profil du candidat :
Le candidat doit être inscrit en dernière année d’un Master ou d’un diplôme d’ingénieur dans un domaine lié à l’informatique ou aux mathématiques appliquées, et posséder de solides compétences en programmation. Une expérience en informatique pour la Science des Données, apprentissage profond, notamment sur graphes, sera un plus.

Formation et compétences requises :
Le candidat doit être inscrit en dernière année d’un Master ou d’un diplôme d’ingénieur dans un domaine lié à l’informatique ou aux mathématiques appliquées, et posséder de solides compétences en programmation.

Adresse d’emploi :
Le stage sera effectué au GREYC (Caen) ou au LITIS (Rouen) en fonction du lieu de recrutement de l’étudiant. Il débutera en février ou mars 2024 pour une durée de 6 mois et bénéficiera d’une gratification au tarif minimum réglementaire pour les stages.

Document attaché : 202312181731_stageMasterGNN-chemo.pdf

Offre en lien avec l’Action/le Réseau : DOING/– — –

Laboratoire/Entreprise : SAMOVAR/Télécom SudParis
Durée : 6 mois
Contact : romerojulien34@gmail.com
Date limite de publication : 2024-02-28

Contexte :
Job recommendation is the task of associating candidates with
jobs. This can be useful for candidates who would like to find to best possible
jobs, for companies that want to find the rarest talents in the vast pool of
candidates, but also for independent recruiters who need to be as precise as
possible when they send a resume to a company.
In this internship, you will work on a new dataset for job recommendations.
Its particularity is that it contains much additional information about candi-
dates and jobs we can represent as a graph. Besides, it is very sensitive to the
cold start problem: We have many new candidates and new jobs, and it restricts
a lot of the algorithms we can use.
If we consider video recommendations on Youtube, an average viewer watches
many videos, and each video is viewed many times. Therefore, when recom-
mending new videos to a specific user, we can look at what other similar view-
ers watched and recommend the most relevant video. This is the principle of
collaborative filtering. In our case, our users are likely to get a job and never
come back. Likewise, jobs are associated with one person, and then, we are
done with it. Therefore, we need to exploit extra information to make the
recommendation.
For our dataset, we can represent our pool of candidates and jobs with a
heterogeneous graph, connecting candidates and jobs, but also additional node
types like skills, cities, or employment types. Because we have this expressive
representation, we must adapt the existing algorithms. During the internship,
we will see how graph neural networks can be used to make recommendations,
and we will propose a new architecture to solve our specific problem.
The goal of this internship will be to publish a paper at an international
conference. The intern will work together with a Ph.D. student.

Sujet :
Job recommendation is the task of associating candidates with
jobs. This can be useful for candidates who would like to find to best possible
jobs, for companies that want to find the rarest talents in the vast pool of
candidates, but also for independent recruiters who need to be as precise as
possible when they send a resume to a company.
In this internship, you will work on a new dataset for job recommendations.
Its particularity is that it contains much additional information about candi-
dates and jobs we can represent as a graph. Besides, it is very sensitive to the
cold start problem: We have many new candidates and new jobs, and it restricts
a lot of the algorithms we can use.
If we consider video recommendations on Youtube, an average viewer watches
many videos, and each video is viewed many times. Therefore, when recom-
mending new videos to a specific user, we can look at what other similar view-
ers watched and recommend the most relevant video. This is the principle of
collaborative filtering. In our case, our users are likely to get a job and never
come back. Likewise, jobs are associated with one person, and then, we are
done with it. Therefore, we need to exploit extra information to make the
recommendation.
For our dataset, we can represent our pool of candidates and jobs with a
heterogeneous graph, connecting candidates and jobs, but also additional node
types like skills, cities, or employment types. Because we have this expressive
representation, we must adapt the existing algorithms. During the internship,
we will see how graph neural networks can be used to make recommendations,
and we will propose a new architecture to solve our specific problem.
The goal of this internship will be to publish a paper at an international
conference. The intern will work together with a Ph.D. student.

Profil du candidat :
The intern should be involved in a master’s program and have
a good knowledge of machine learning, deep learning, natural language processing, and graphs. A good understanding of Python and the standard libraries
used in data science (scikit-learn, PyTorch, pandas, transformers) is also expected. In addition, a previous experience with graph neural networks would be appreciated.

Formation et compétences requises :
The intern should be involved in a master’s program and have
a good knowledge of machine learning, deep learning, natural language processing, and graphs. A good understanding of Python and the standard libraries
used in data science (scikit-learn, PyTorch, pandas, transformers) is also expected. In addition, a previous experience with graph neural networks would be appreciated.

Adresse d’emploi :
Télécom Sudparis, Palaiseau

Document attaché : 202312181300_internship_job_recommandation-2.pdf

Offre en lien avec l’Action/le Réseau : DOING/– — –

Laboratoire/Entreprise : SAMOVAR/Télécom SudParis
Durée : 6 mois
Contact : romerojulien34@gmail.com
Date limite de publication : 2024-02-28

Contexte :
In this internship, we propose to study the problem of financial forecasting, i.e., predicting the future
variation of the price of a financial instrument, using deep learning. The student will work on a new
data source with a finer granularity than existing datasets. Because of the difficulty of obtaining
data, previous works focused on price prediction at the scale of a day, a week, or a month. Our
new dataset contains intraday information. Therefore, we can predict the price within a day and
use multi-scale analysis. Besides, our new dataset contains different kinds of financial instruments
(FOREX, crypto, options, futures) and additional information about the companies (description,
financial reports, dividends).

Sujet :
In this internship, we propose to study the problem of financial forecasting, i.e., predicting the future
variation of the price of a financial instrument, using deep learning. The student will work on a new
data source with a finer granularity than existing datasets. Because of the difficulty of obtaining
data, previous works focused on price prediction at the scale of a day, a week, or a month. Our
new dataset contains intraday information. Therefore, we can predict the price within a day and
use multi-scale analysis. Besides, our new dataset contains different kinds of financial instruments
(FOREX, crypto, options, futures) and additional information about the companies (description,
financial reports, dividends).

Profil du candidat :
The intern should be involved in a master’s program and have a good knowledge of machine learning,
deep learning, and data processing. A good understanding of Python and the standard libraries used
in data science (scikit-learn, PyTorch, pandas) is also expected. A previous experience with finance
is appreciated but not required for this internship.

Formation et compétences requises :
The intern should be involved in a master’s program and have a good knowledge of machine learning,
deep learning, and data processing. A good understanding of Python and the standard libraries used
in data science (scikit-learn, PyTorch, pandas) is also expected. A previous experience with finance
is appreciated but not required for this internship.

Adresse d’emploi :
Télécom SudParis, Palaiseau

Document attaché : 202312181259_stage_finance.pdf

Offre en lien avec l’Action/le Réseau : BigData4Astro/– — –

Laboratoire/Entreprise : DAVID Lab – UVSQ – Versailles
Durée : 6 mois
Contact : mustapha.lebbah@uvsq.fr
Date limite de publication : 2024-01-31

Contexte :
In this research internship, we aim to test the feasibility of a modern neural methodology based on the generative model, which has been successfully applied to text/image processing. The field of video generation technology has seen significant advancements, with modern models capable of producing highly realistic videos [1, 4, 5]. Drawing an analogy to this, studying the life cycle of an aircraft engine can be viewed similarly to creating a video. In this analogy, each frame represents a distinct flight undertaken by the aircraft, during which multiple continuous parameters forming multivariate time series data. Each multivariate time series can be compared to a frame in a video, reflecting the dynamic states of the aircraft engine during the respective flight.

Sujet :
The aim of this research internship is to strengthen collaboration with Safran.

-Study the current state of the art in deep generative model and multivariate time series,
-By sequentially analyzing this collection of parameters flight after flight, akin to stringing together video frames, we can create a detailed and comprehensive depiction of the aircraft engine’s life cycle, allowing for the identification of behavioral patterns, anomalies and providing predictive insights into the engine’s performance and longevity.
-Based on previous studies [2, 3], implement one or more algorithms/architectures. The results obtained during the internship may lead to contributions to open-source software, or even a scientific publication, depending on the intern’s skills and motivation.

Profil du candidat :
End of engineering degree, M1/M2 in data science, statistics, artificial intelligence, or computer science. Excellent understanding of machine learning basics, particularly deep learning models. Excellent programming skills, especially with tensorflow/keras.

Formation et compétences requises :
End of engineering degree, M1/M2 in data science, statistics, artificial intelligence, or computer science. Excellent understanding of machine learning basics, particularly deep learning models. Excellent programming skills, especially with tensorflow/keras.

Adresse d’emploi :
The internship will be in the DAVID Lab at the University of Versailles

Document attaché : 202312150838_DAVID-UVSQ-Research_Internship_GenerativeMTS.pdf