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Job offers

3-Year PhD position at IRSTEA:

3-Year PhD position in Saint-Etienne:

Probabilistic study of instantiated gaussian processes and application to spatio-temporal data.

Starting date: September or October 2017 Application deadline date: April 14th 2017

Decision announcement date: June 1st 2015


The thesis will take place in the Saint-Etienne part of Camille Jordan Institute. The research will be undertaken in the context of an interdisciplinary project involving also Hubert Curien Laboratory from the University Jean Monnet of St Etienne.

The consortium has scientific expertise on probability and statistics, information and image processing, and machine learning, providing a stimulating scientific environment for this thesis. Last but not least, St Etienne is a very pleasant place to study and work. St Etienne is rated each year as one of the best place in France for studying.

PhD thesis subject

Gaussian processes are non-linear models of continuous random processes which are widely used to describe numerical data as sounds, images, videos, etc. (see for e.g. [W08,Z16]).

A Gaussian process is defined mainly by its expectation function and its covariance function (the kernel).

The description of the kernel using parametric functions and the estimation of these parameters form the focus of many recent works [L05,D16].

In the context of image sequences (knowing that our study is intended to address other types of data), the main objective is no longer to describe a Gaussian process but a set of Gaussian processes that can possess instances (Different temporal or spatial supports), with the aim to analyse videos with dynamic textures (lights, waves, clouds, fields of wheat …) taken from different angles for example.

The main objective of the thesis is to provide a precise mathematical framework for these instanciated Gaussian processes in order to be able to estimate the different parameters (instances, mathematical expectations and kernels' parameters).

First, the PhD student will be intended to make a state-of-the-art about the different kernels and their properties, mainly their stationarity in time and space in order to propose new kernels. The next step is to develop robust parameter estimation methods and to work on the automatic selection of the kernels. Then, the formalism of non-stationary and instanciated Gaussian processes will be developed, together with their numerical simulations. The last step concerns the mixture of instanciated Gaussian processes and their application to real data like videos.


We are looking for a motivated student holding a Master degree (on the 1st of September 2015) in the field of applied mathematics (probability, data analysis, estimation and optimization, …) or “computer science” (or “computer vision”) with strong skills in applied mathematics. A good background in software development (algorithmic, Matlab/Octave/Scilab or Python, …) is expected. Knowledges in image processing and machine learning would also be appreciated.


Net salary: around 1400 euros without teaching activities and around 1650 euros with teaching activities (64 hours per year).

Application process

Your application should include the following documents: - Letter of intent - Grades and ranking during Master 1 and Master 2 - Scientific CV - List of publications (if it exists of course) - Names of Referees (at least 2)

Contacts: - tugaut@math.cnrs.fr (http://tugaut.perso.math.cnrs.fr/accueil.html) - olivier.alata@univ-st-etienne.fr (http://perso.univ-st-etienne.fr/ao29170h/)


[D16] N. Durrande1, J. Hensman, M. Rattray, N. D. Lawrence, “Detecting periodicities with Gaussian processes.” PeerJ Computer Science 2:e50 https://doi.org/10.7717/peerj-cs.50. [L05] Neil Lawrence, “Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models.” Journal of Machine Learning Research 6 (2005) 1783–1816. [W08] Jack M. Wang, David J. Fleet and Aaron Hertzmann, “Gaussian Process Dynamical Models for Human Motion.” IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 30, no. 2, Feb. 2008. [Z16] Ziqi Zhu, Xinge You, Shujian Yu, Jixin Zou and Haiquan Zhao, “Dynamic texture modeling and synthesis using multi-kernel Gaussian process dynamic model.” Signal Processing, Vol. 124, July 2016, Pages 63–71. Big Data Meets Multimedia Analytics — Containing a selection of papers from the 21st International Conference on Multimedia Modelling (MMM2015).

March 2017

C. Geuzaine and Xavier.Antoine@univ-lorraine.fr are proposing a joint Ph.D. thesis with on some numerical methods in optics (classical/quantum) in the framework of Inria. The potential position is located mainly in Nancy but through a close co supervision with the University of Liège. All the information is available at


where the students can apply.

IFP Energies nouvelles propose un stage en 2017 en régression parcimonieuse et réduction de dimension appliquées à la normalisation de mesures instrumentales et de données présentant un facteur d'échelle différent. L'objectif est de l'estimer en présence de données manquantes et aberrantes, et de bruits, pour des signaux courts, présentant des variations d'amplitude importantes. Cette estimation doit se faire de la manière la plus automatisée possible, en se basant sur les propriétés et des a priori sur les données (parcimonie, positivité)

Le sujet est décrit (en anglais), sur la page :


PhD Student position in Numerical Simulation at LJLL (Paris)

# Links for full description : https://www.ljll.math.upmc.fr/~privat/documents/sujet_these_LRGM.pdf

PhD student position in Machine Learning for Geosciences [ERC project]

We are searching for an outstanding candidate with a strong interest in machine learning and geosciences to cover one PhD student position to join the Image and Signal Processing (ISP) group in the Universitat de Valencia, Spain, http://isp.uv.es. The position is fully funded by an ERC Consolidator Grant 2015-2020 entitled “Statistical Learning for Earth Observation Data Analysis” (SEDAL), http://isp.uv.es/sedal.html, under the direction of Prof. Gustau Camps-Valls.

  • The project and job description

We aim to develop the next generation of statistical inference methods to analyze Earth Observation (EO) data. Machine learning models have helped to monitor land, oceans, and atmosphere through the analysis and estimation of climate and biophysical parameters. Current approaches, however, cannot deal efficiently with the particular characteristics of remote sensing data. We will develop advanced regression (retrieval, model inversion) methods to improve efficiency, prediction accuracy and uncertainties, encode physical knowledge about the problem, attain self-explanatory models, learn graphical causal models to explain the complex interactions between essential climate variables and observations, and discover hidden essential drivers and confounding factors in Climate/Geo Sciences.

Highly motivated researchers with a degree in computer science, statistics, machine learning, electrical engineering, physics, or mathematics are encouraged to apply. All candidates should have a solid understanding and knowledge of machine learning and statistics, and being particularly interested in remote sensing and geoscience problems. The thesis will address problems in regression, graphical models and causal inference. Good programming skills (Matlab/Python/R/C++), a critical and organized sense for data analysis, as well as maturity and commitment, strong communication, presentation and writing skills are a big plus.

  • Application details

- Deadline: Send your application no later than April 1st 2017. - How? Send me: 2-pages CV, motivation letter, papers if any, and one recommendation letter or contact - When? Preferred starting dates: June 2017 - How long? 3 years contract - How much? Salary according to UV scales including social security, health insurance benefits, and travel money - Where? Valencia, Spain, Mediterranean city, nice weather, hike and beach. Excellent cost-of-living index = 55

  • Contact

- Before applying: Informal inquiries may be addressed to Prof. Dr. Gustau Camps-Valls, gustau.camps@uv.es - Ready to apply? Send your dossier in one single PDF to gustau.camps@uv.es, subject: “SEDAL application”

Onera in collaboration with Telecom Paristech invites applications for a PhD studentship to undertake research in the fields of machine learning and remote sensing. Subject is “Deep networks for multi-temporal activity analysis of Earth-observation data”.

# Short description : Last years have seen the massive adoption of deep learning techniques for various tasks in computer vision. In remote sensing and Earth-observation data analysis, our team has developed algorithms for classification and detection which have established new state-of-the-art performances. With this new PhD thesis, we now want to discover how deep networks can help understanding the multitemporal satellite image series.

Research axis will include : * Semantic classification of aerial and satellite images * Deep Learning architectures * Investigating standard tools of image comparison in the context of deep network analysis. * Big data

# Links for full description : https://www.adum.fr/as/ed/voirproposition.pl?site=adumfr&matricule_prop=14566 http://sites.onera.fr/formationparlarecherche/theses-dtim (ref. TIS-DTIM-2017-008)

The successful candidates will work with : Pr. Yann Gousseau http://perso.telecom-paristech.fr/~gousseau/index_eng.html Alexandre Boulch https://sites.google.com/view/boulch/home Bertrand Le Saux http://www.onera.fr/en/staff/bertrand-le-saux at Onera ( http://www.onera.fr/en ) and Telecom Paris Tech ( https://ltci.telecom-paristech.fr/en/ ), located in Palaiseau, near Paris, France .

PhD Proposal - UQAM, Canada

The use of Determinantal point processes for Monte-Carlo experiments and computer experiments

Jean-François Coeurjolly & Pierre-Olivier Amblard

Objet: NPL-Cambridge CASE PhD Studentship 'Vegetation assessment using machine learning techniques on spectral imaging data' Date: 28 février 2017 à 20:07:37 UTC+1

NPL-Cambridge CASE PhD Studentship 'Vegetation assessment using machine learning techniques on spectral imaging data’

See also http://www.jobs.cam.ac.uk/job/13010/

It is estimated that by 2050 70% of more food needs to be produced worldwide. It is therefore not only essential to use our resources as efficiently as possible, but also to assess and mitigate the risk to crops. Farming is increasingly driven by machines and with less staff it is difficult for farmers to monitor their crop. There is the drive to use airborne technologies as well as satellite imagery to do this remotely and automate this. The project aims to build up an extensible, probabilistic framework to do this resulting in a database and data model.

The ultimate aim is to not just have a database of the spectral signatures of different plant species, but also to incorporate phenology and health status of the plants. With regards to crops this will help mitigate the risks of droughts and diseases. Irrigation can be directed where needed and fertilizer used more effectively. Early intervention can stop diseases spreading and there will be less use of pesticides and fungicides. Being able to choose an optimal time to harvest will lead to less food wastage.

Using sophisticated machine learning techniques, sparse models of the land cover can be created. These models will help where there is limited up and downlink bandwidth as there are with airborne technologies as well as with satellites. The device gathering the data can carry a sparse model and only where new data is significantly different to the model action is necessary. In the first instance this action will be an alert to an anomaly. Further analysis is then necessary whether the anomaly is expected due to e.g. change of the season, or the anomaly needs intervention or the model needs updating (e.g. a change of crop).

The aim of this PhD project is a probabilistic model of spectral signatures of plants incorporating phenology and diseases. To this end various machine learning techniques will be employed and benchmarked against each other. Data from the CropScape database (https://nassgeodata.gmu.edu/CropScape/) will be combined with data from Avaris (http://aviris.jpl.nasa.gov/alt_locator/) for initial analysis. This can then be enriched with data from the Sentinel satellites for temporal analysis, since the revisit times are shorter. Different resolutions and number of spectral bands need to be given consideration. The techniques will also be assessed on their ability to generate knowledge automatically, for example in which way the spectral signature of a plant changes under increasing drought conditions and whether there are underlying general principles. Another aspect is the development of a confidence measure identifying and quantifying classification error.

The successful candidate will be working closely with David Coomes (Cambridge), Anita Faul (Cambridge), Alistair Forbes (NPL), and Carola-Bibiane Schönlieb (Cambridge).

Applicants should have a masters degree in mathematics or a closely related subject, e.g. an engineering degree with a strong mathematical foundation, and should be UK or EU nationals.

The funds for this studentship are available for 3 years in the first instance.

In order to be considered for this studentship please submit a formal application to the PhD in Applied Mathematics and Theoretical Physics, University of Cambridge via the University's Graduate Admissions website (for more information on this please visit http://www.graduate.study.cam.ac.uk/how-do-i-apply); and send an expression of interest email to grad-administrator@maths.cam.ac.uk which explains why you are interested in this studentship.

Applications should be submitted online until the 31st of March 2017. Expressions of interest letter that briefly describe your motivation for this project should be sent to grad-administrator@maths.cam.ac.uk by the same date.

Please quote reference LE11533 on your application and in any correspondence about this vacancy.

The University values diversity and is committed to equality of opportunity. The Department would particularly welcome applications from women, since women are, and have historically been, underrepresented on our student cohort.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

DEPARTMENT/LOCATION Department of Applied Mathematics and Theoretical Physics

REFERENCE LE11533 ; CATEGORY Studentships ; PUBLISHED 23 February 2017 ; CLOSING DATE

 31 March 2017 

February 2017

PHD Proposal

Title: Accelerating kinetic model construction and better process control by extracting information from transient data

Place: IFPEN, Rond point de l’échangeur, BP 3 - 69360 Solaize - France

PhD position at IFP Energies nouvelles (IFPEN)

The production of ultra-low sulfur diesel is a major challenge for economic and societal perspective. The hydrocracking process is dedicated to maximize the production of such pools. The use of kinetic models is mandatory in order to design and control such processes. These models must be recalibrated for each catalyst generation. This requires the use of long pilot plant tests (several months) and expensive (several hundred k€). The relatively long duration of the tests (1.5 to 2 months) is partly due to the catalyst stabilization at each operating conditions modification (several days) and secondly to the supervision of the test in order to reach targets (conversion efficiency). The objective of this thesis is multiple:

1. Optimize the operating conditions to maximize the information (design of experiments) and use the points in transient mode (points currently acquired to check the stability but not used) for kinetic models fitting

2. Optimize the supervision of pilot plants to reach quickly some targets (conversion, yields)

This will be carried out with following program:

1. Study experimental data in order to provide a catalyst stabilization mechanism

2. Numerical study in order to build the model, test and develop methods for on line identification and develop methods for design of experiments

3. Validation with experimental data

Desired Skills and Experience:

  • Degree in Process System Engineering, Data Mining/Data Science or Computer Science
  • Experience with one or more of the following: Programming skills, Process Engineering, Process Control
  • English skills
  • Motivation to work in a transdisciplinary team

Supervisors: Dr. Benoit Celse, Benoit.celse@ifpen.fr ; Dr. D. Guillaume, denis.guillaume@ifp.fr; Pr. J. Thybaut, Joris.Thybaut@UGent.be

Interested candidates are requested to send a detailed CV, one recommendation letter and university/master transcripts to Benoit Celse at benoit.celse@ifpen.fr.

Industrial PhD internship

The purpose of the internship is to developp a model to measure the economic power of the different agencies of the Bank La Caisse d'Epargne – Rhônes Alpes, that will take into account different external variables (INSEE indices, population transformation, density of rival agencies, …), the caracteristics of the agencies and consommation behaviors of the customers. This model should allocate the cost masses uniformly on the considered agencies.

This internship can create opportunities for further doctoral investigations (typically with a CIFRE funding).

Key-words : Generalized linear model, logistic regression, variable selection Academic supervisors : Cristina Butucea (CREST) and Clément Marteau (Université Lyon 1). Contact: marteau@math.univ-lyon1.fr

L'Observatoire de Paris offre un contrat de post-doc/ingénieur à durée déterminée d’un an, renouvelable plusieurs années, au laboratoire GEPI à Meudon pour travailler au développement du traitement de données spectroscopiques du satellite Gaia, mission de l’Agence Spatiale Européenne (ESA).

Une description plus détaillée du poste est disponible à l’adresse http://gaia.obspm.fr/qui-fait-quoi/article/postes-a-pourvoir-spectrocopie

December 2016


November 2016

Veuillez trouver le lien vers un poste externe, ouvert en CDI au Centre National d’Etudes Spatiales à Toulouse : « Ingénieur Etudes et Développements de Systèmes de Valorisation des Données ».

La division Energy du groupe de de conseil en innovation et ingénierie Altran Technlogies en France, est actuellement en recherche active de nouveaux consultants pour des missions de longue durée chez les grands acteurs du secteur énergétique (Engie, Total, EDF, Technip,…).

Plus particulèrement, Altran recherche des candidats pour des missions chez Engie (ex-GDF Suez) en région parisienne avec un profil statistiques, mathématiques ou modélisation afin de travailler sur la thématique des marchés du gaz et de l'électricité.

Vous pouvez directement envoyer votre CV à l'adresse suivante: lucile.lepautrematATaltran.com

Data Science - Design, Développement et implémentation d’un algorithme d’analyse de données de consommation d'énergie

PhD proposal (convention CIFRE), Computer Science

Self-Organized Representations of Dynamical Environments for a Welcoming Task by an Autonomous Robot

Post-doc position in machine learning in Grenoble, France

Title: Learning welding prediction and classification models from various, heterogeneous sources Duration: 2 years Partners: LIG UGA, LJK UGA, Total Starting date: January 2017 (if possible)

Description: Machine learning methods are meeting an increasing success in various domains, such as marketing with customer behavior prediction, health with patient diagnosis and industry with the optimization of industrial processes. The present project fits within a general problem addressed by Total on trying to predict, from various characteristics (or parameters/variables), different properties (as mechanical properties under stress) of welding in pipelines. The parameters can take various forms (quantitative or qualitative, ordinal or non-ordinal, real or Boolean) and are highly heterogeneous. They however need to be combined in order to obtain good prediction and one of the main challenges of this project is precisely to find the best way to combine different parameters for enhanced prediction and classification. In parallel, it is of course important to determine whether the different parameters are correlated or not, and to make use of possible correlations in the prediction/classification tasks. The developed method will have to be well adapted to large scale, heterogeneous datasets that are common to many different domains; it will furthermore be applied to the prediction of weld properties from paramters of the welding process.

During the project, the successful candidate will have to address the following points:

     1.	Study correlations between variables of many different types and extend existing models/methods to integrate all data types as well as their dependencies. The dataset collected by Total for studying welding in pipelines is unique by the diversity of the variables it relies on (product names, physical measures, manual annotations, …). This diversity constitutes a major challenge for all existing data analysis and machine learning methods. We will also try, whenever possible, to quantify the uncertainty associated with the representation of each data type;
     2.	In addition to the above-mentioned datasets, physical phenomena (as welding) are often described via equations that display relations between variables; they are also subject to simulations aimed at assessing their future evolution. One of the goals of the project will be to study how one can couple machine learning and physical equations and simulations to improve the accuracy of the prediction. This is a promising line of research that can bring together communities that do not usually work together;
     3.	Provide tools to help experts understand the results obtained by the models developed.

This will include:

   ●	Working with a team of computer scientists and mathematicians
   ●	Developing new machine learning/data analysis models
   ●	Implementing and testing the models developed

Required skills: Ph.D. or equivalent experience in computing, modeling, machine learning, statistics and applied mathematics

Application: The application should include a CV mentioning the publications, and any relevant documents. Candidates are encouraged to provide contact information to reference persons. Please send your application in one single pdf to Marianne.Clausel@imag.fr and Eric.Gaussier@imag.fr

October 2016

Merci de bien vouloir diffuser cette offre d'emploi à pourvoir immédiatement. Il s'agit d'un CDD de 1 an qui pourra évoluer en CDI. Les candidatures doivent être envoyées à mon adresse: dugenie@cines.fr.

Bien cordialement. Pascal Dugénie

Le CINES héberge, administre et met à la disposition de la communauté Enseignement supérieur et Recherche un ensemble d’équipements de calcul intensif de très grande puissance, parmi lesquels le supercalculateur « OCCIGEN », un des clusters de calcul les plus puissants d’Europe. L’administration de ces équipements extrêmes requiert des compétences expertes et diversifiées : systèmes, réseau d’interconnexion, systèmes de fichiers parallèles, middlewares de programmation parallèle, ordonnanceur de travaux, techniques d’optimisation des codes de calcul, etc. Les services du Centre sont accessibles par des liaisons à très haut débit sur le nœud du réseau national de la recherche Renater, 24h/24 et 7J/7. Dans ce contexte, la personne mise à disposition participera à l’expertise et à la promotion de l’utilisation des techniques de simulation et de calcul parallèle auprès de la communauté Enseignement Supérieur-Recherche. Elle sera également sollicitée sur des tâches liées à l’administration des systèmes. Il s’agit d’un poste d’ingénieur d’études en calcul scientifique est à pourvoir en régie pour une durée de 1 an, éventuellement renouvelable, au Centre Informatique National de l’Enseignement Supérieur (CINES) situé à Montpellier, 950 rue de Saint-Priest. Il est proposé à un(e) titulaire d’un diplôme de niveau bac+4 en informatique scientifique ayant déjà une première expérience dans le domaine.

Missions, activités - Participer aux diverses activités de support aux utilisateurs HPC : assistance, accompagnement, conseil et expertise auprès des chercheurs dans le développement, le portage, l’optimisation ou le « profiling » de leurs codes ; - Evaluer et sélectionner les outils, logiciels et bibliothèques de calcul, de génération ou d’optimisation de code, etc. pertinents pour les utilisateurs ; - Contribuer à l’expertise des dossiers de demande d’accès aux ressources de calcul ; - Rédiger des rapports techniques ; - Assurer le bon fonctionnement des environnements de calcul : calculateurs proprement dits, périphériques et données associées.

Compétences et aptitudes requises Connaissance approfondie et pratique réelle des langages C, C++ et Fortran 90 ; Bonne connaissance pratique de l’utilisation des systèmes de type Unix et des langages de script Shell ; Bonne connaissance des architectures actuelles de clusters HPC ; Très bon relationnel, autonomie et capacité d’initiative ; Aisance dans l’expression orale et écrite ; Aptitude au travail en équipe ; Maîtrise de l’anglais technique du domaine ; Pratique courante des outils bureautiques.

Compétences, aptitudes ou expériences complémentaires souhaitées - Expérience pratique – au moins 5 ans - des techniques de calcul parallèle sur cluster Unix (64 nœuds ou plus) ; - Expérience des environnements de calcul sur XeonPhi ; - Connaissance du contexte institutionnel Enseignement supérieur et Recherche.

September 2016

PhD postion at the University of Edinburgh

Area: Big Data Optimization, Machine Learning, Randomized Methods in Optimization, Randomized Numerical Linear Algebra

Starting Date: As soon as possible (before or on January 1, 2017)

PhD Supervisor: Peter Richtarik

Application Procedure: Apply for PhD in “OR & Optimization” via our online application form: http://www.maths.ed.ac.uk/school-of-mathematics/studying-here/pgr/phd-application

There is no application deadline. Applications will be reviewed as they arrive, and the position will be open and advertised until a suitable candidate is found and the post is filled. You may consider sending an email to Peter Richtarik before or after you apply.

Kind regards,
Peter Richtarik
Head, Big Data Optimization Group
EPSRC Early Career Fellow in Mathematical Sciences
The School of Mathematics
University of Edinburgh
6317 James Clerk Maxwell Building
Peter Guthrie Tait Road
Edinburgh, EH9 3FD

August 2016

Thèse financée Epidémiosurveillance these_epidemiosurveillance_2016.pdf

July 2016

Un poste d'ingénieur est ouvert dans le cadre du déploiement d'un système de prévision et d'analyse des pollutions environnementales. Le déploiement s'appuie à la fois sur les masses de données et sur la simulation numérique, de l'échelle mondiale à l'échelle urbaine. Le système est exigeant en terme de calculs et doit être optimisé. En particulier, l'étape de fusion des masses de données et des simulations numériques peut être très coûteuse et requiert une parallélisation adaptée. Les résultats des calculs sont notamment mis à disposition du grand public dans une application mobile sous Google Play et App Store, ce qui contraint à pouvoir répondre à des dizaines de milliers de requêtes par minute.

Le travail sera mené en collaboration étroite avec la start-up Ambiciti et dans le cadre d'un projet EIT Digital impliquant la PME Numtech (qualité de l'air, météorologie), Forum Virium (Helsinki), Inria@SiliconValley, The Civic Engine (San Francisco) et Cap Digital.

Le poste s'adresse à des ingénieurs, avec ou sans expérience, capables d'être productifs et de s'adapter dans un contexte dynamique et évolutif.

Plus d'informations sont disponibles ici: http://www-rocq.inria.fr/clime/jobs/2016/engineer-position.pdf

June 2016

Notre laboratoire, le laboratoire national de métrologie et d’essais est à la recherche d’un profil jeune diplômé dans le cadre d’un CDD à pourvoir sur notre site de trappes dans le domaine des mathématiques appliquées

Audrey PASQUIER Chargée de Développement RH Direction des Ressources Humaines Tél. : 01 30 69 12 70

Laboratoire national de métrologie et d'essais

29, avenue Roger Hennequin - 78197 Trappes cedex

Tél. : 01 30 69 10 00 - Fax : 01 30 69 12 34

Site internet : www.lne.fr

CDD situé à Trappes (78)

Durée : 6 mois

L’entreprise : www.lne.fr

Au carrefour de la science et de l’industrie depuis sa création en 1901, le LNE offre son expertise à l’ensemble des acteurs économiques impliqués dans la qualité et la sécurité des produits.

Le LNE en quelques chiffres : 780 collaborateurs. 5 métiers (la Mesure, les essais, la certification, la formation et la R&D). 8 domaines d’intervention (Métrologie, Santé, Construction, Environnement, Electrotechnique, Transports, Emballages et conditionnement, Produits de consommation). 55 000 m2 de laboratoires (dont 10 000m2 à Paris et 45 000m2 à Trappes). 7 implantations (2 sites en Ile de France, 2 délégations régionales à Poitiers et Nîmes, 1 antenne à St Etienne, 2 filiales à Washington et Hong Kong) Près de 9000 entreprises clientes

Contexte :

Suite à un appel à projet fructueux au premier trimestre 2016, le LNE se voit financer le projet LEICA par le labex FIRST-TF. Ce projet de collaboration entre le service mathématique et statistique (SMS) du LNE et le SYRTE a pour but le développement et la mise à disposition d’un logiciel avancé d’évaluation de l’incertitude à destination de l’enseignement supérieur.

Le SMS a mis en ligne début 2016 un premier logiciel LNE-MCM d’évaluation de l’incertitude par méthode Monte-Carlo à destination de la communauté métrologique. Ce logiciel déjà testé en formation rencontre un certain succès au sein des laboratoires de métrologie. Il suscite l’intérêt croissant d’autres communautés industrielles et enseignantes. En effet, l’évaluation d’incertitude a été introduite depuis quelques années dans les programmes scolaires et universitaires. Le développement de logiciels sur ce thème doit permettre de faciliter son enseignement et son assimilation par les étudiants. Le projet LEICA a ainsi pour objectif d’enrichir le logiciel existant LNE-MCM pour lui ajouter les fonctionnalités attendues par ces nouvelles communautés d’utilisateurs. Le résultat en sera une distribution nettement plus large du nouveau logiciel que ne l’est déjà LNE-MCM, notamment à l’international avec une version anglaise.

Missions confiées :

Au sein du service mathématiques et statistiques du LNE, vous participerez au développement d’une application MATLAB d’évaluation de l’incertitude.

Vous effectuerez les missions suivantes :

- Appropriation du logiciel LNE-MCM précédemment développé au sein du LNE

- Etude de la bibliographie liée aux méthodes à implémenter

- Implémentation sous MATLAB des fonctionnalités retenues

- Test et validation de la nouvelle application

- Rédaction du manuel utilisateur

- Version anglaise du logiciel et du manuel utilisateur

Pour réaliser ce travail, vous serez encadré(e) par une équipe projet ayant déjà une forte expérience de développements d’applications sous MATLAB et responsable du cahier des charges de l’application.

Profil :

De profil Ingénieur en statistiques ou mathématiques appliquées, ou master 2 mathématiques appliquées, vous possédez de compétences solides en statistiques et probabilités, de même qu’une bonne maîtrise écrite et orale de l’anglais. De plus, vous avez de très bonnes connaissances en programmation scientifique sous MATLAB, une expérience antérieure de l’outil d’interface GUIDE de MATLAB serait un atout. Vous avez de solides capacités rédactionnelles et de synthèse. Autonomie et qualités relationnelles seront également des qualités essentielles pour mener à bien vos missions. Quelques déplacements sur notre site de Paris 15ème sont à prévoir. Pour candidater, merci d’envoyer votre CV et LM à recrut@lne.fr en indiquant la référence CL/SMS/DG

Industrial PhD position (CIFRE fundings) with TecKnowMetrix company located at Voiron.

Presentation of the company and the context:

TecKnowMetrix (TKM) is an innovative company created by the University Pierre Mendes France and INRA, based at Voiron. The company designs and develops methods and tools for the analysis of scientific and technological information. The application of these methods to large corpora enables it to deliver an overall view of a complex technological environment.

TecKnowMetrix has developed a software platform that can convert into a common format heterogeneous data sources. Indeed, the scientific and technical information is helpful in steering research public laboratories or innovative industrial groups. But its use is complicated by the diversity of sources, the heterogeneity of extremely important information formats and volumes that must be handled. This point refers to what is commonly called the “big data” topic on which the French actors must absolutely take strategic positions in the global competitive environment.

The TKM platform provides the ability to store and organize in a unique knowledge base that information from various sources. Once the database is created knowledge, TKM platform offers many ways to access relevant information: semantic analysis, data mining, statistical analysis or mapping information. TecKnowMetrix develops and uses this analysis and visualization methods to restore customers a synthetic vision and can be used directly in a given technological field.

Thesis project:


Automatic classification is a key issue in a “big data” environment (large and heterogeneous data), then it is vital to have powerful and efficient tools for automatic classification.

The consultants of the company, before starting a study, appraise records selected for their study and classified so as to analyze them. This classification step is time consuming but allows to provide relevant analysis to the client.

There are different ways for document classification, the longest but certainly the most reliable is that of reading each document to assign it to one or more classes. Methods of automatic or semi-automatic classification was introduced in TKM. They are based on document metadata or search queries. The results of these automated rankings can then be changed manually.

These methods pose increasingly problems for TKM. Given the amount constantly growing information to be processed, manual methods are no longer sufficient and semi-automatic classification can be manually corrected effectively. Added to this is the fact that each new mission, we must build a new classification. Finally, automatic methods currently used in TKM only consider the metadata but never textual content, gold, thanks to this ranking is more accurate and more relevant.

Goal :

The aim of the thesis is therefore to design an automatic classification method allowing:

- The extraction of keywords / concepts / phrases in a multi-source documentary corpus.

- Automatic creation of thesaurus based on the identified concepts (Aeronautics, Information Technology, Health, etc.)

- The document grouping in these thematic ontologies

It all making unsupervised manner.


One of the challenges of this project will provide a self-learning module based on the latest machine learning technologies can not only classify documents but also to create new thematic ontologies based on the identified concepts.

Other issues related to the profession of TKM will determine under what ontologies document to be classified. Indeed, the same document can be classified different technology classes (example: software for handling DNA sequences must logically be classified Health and Informatics)

Finally, the last challenge will be designing a module capable of handling multi-source documents whose language is very specific to each source.


If you are interested send an email to Marianne.Clausel@imag.fr and Massih-Reza.Amini@imag.fr by providing a CV, a motivation letter and your semester S3 marks.

joboffers.txt · Last modified: 2017/04/19 09:50 by jbdurand
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