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joboffers [2019/06/13 16:44]
jbdurand
joboffers [2020/11/18 15:59] (current)
oudet [Job offers]
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    * // New // https://akatech.tech/announcements,a.html    * // New // https://akatech.tech/announcements,a.html
 +
 +----
 +** November 2020 **
 +
 +// New//: \\ 
 +{{ proj:mscthesis2020_maitre_drapingPhd.pdf |Sujet de thèse à Grenoble, Laboratoire de mathématiques appliquées LJK : Modelling and simulation of immersed surfaces with high order geometric
 +energies using diffusion-redistancing schemes}}
 +
 +
 +----
 +** July 2019 **
 +----
 +
 +{{ {{ :jobs2018:ia_organisations_en.pdf |Industrial CIFRE PhD position in Grenoble - Learning organizations and adaptive performance: combining artificial intelligence and human interaction to support organizational transformation and to improve user experience}}
 +
 +----
 +
 +{{ :jobs2018:annonce_these_stromalab-enit.pdf |Sujet de thèse à Toulouse, Laboratoire STROMALab : Physiopathologie et traitement de données mathématiques}}
 +
 +----
 +
 +{{ :jobs2018:ia-organisations.pdf |Sujet de thèse CIFRE à Grenoble - Organisations apprenantes et performance adaptative : combiner intelligence artificielle et interaction humaine pour accompagner la transformation des organisations et améliorer l’expérience utilisateur}}
 +
 +----
 +
 +[[https://mia.toulouse.inra.fr/images/8/8f/Proposition_these_MIAT-LIPM-0719.pdf|Bourse de thèse INRA : Modélisation et contrôle du priming de l’immunité végétale par stimulation sonore]]
  
 ---- ----
Line 20: Line 46:
 ** June 2019 ** ** June 2019 **
 ---- ----
-// New//: \\  
 [[https://diard.files.wordpress.com/2019/05/bio-bayes-phd-position-diard.pdf|PhD Position: "Bio-Bayes Predictions -- Coupling Biological and Bayesian Predictive Models in Neurocognitive Speech Processing" at LPNC, Grenoble]] [[https://diard.files.wordpress.com/2019/05/bio-bayes-phd-position-diard.pdf|PhD Position: "Bio-Bayes Predictions -- Coupling Biological and Bayesian Predictive Models in Neurocognitive Speech Processing" at LPNC, Grenoble]]
  
-// New//: \\ +---- 
 [[http://www-sop.inria.fr/asclepios/recrutement/Phd_ImmunoTherapy-Inria.pdf|PhD position: "AI based Selection of Imaging & Biological markers predictive of Immunotherapy Response in Lung Cancer" at Inria Sophia Antipolis]] [[http://www-sop.inria.fr/asclepios/recrutement/Phd_ImmunoTherapy-Inria.pdf|PhD position: "AI based Selection of Imaging & Biological markers predictive of Immunotherapy Response in Lung Cancer" at Inria Sophia Antipolis]]
  
-// New//: \\ +---- 
 [[http://dobigeon.perso.enseeiht.fr/proposals/proposal_ANITI_2019.html|PhD Positions in machine learning and signal & image processing at 3IA ANITI, Toulouse]] [[http://dobigeon.perso.enseeiht.fr/proposals/proposal_ANITI_2019.html|PhD Positions in machine learning and signal & image processing at 3IA ANITI, Toulouse]]
  
-// New//: \\ +---- 
 {{ :jobs2018:phdoffer_cifrenaverlig_fairnessrecommendation.pdf |Cifre PhD Proposal: “Fairness in multi-stakeholder recommendation platforms” at NAVER LABS Europe / LIG}} {{ :jobs2018:phdoffer_cifrenaverlig_fairnessrecommendation.pdf |Cifre PhD Proposal: “Fairness in multi-stakeholder recommendation platforms” at NAVER LABS Europe / LIG}}
  
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 ---- ----
 {{ :jobs2018:cdi_docteur_ia_info.pdf |Industrial PhD position at K2 (Grenoble)}} {{ :jobs2018:cdi_docteur_ia_info.pdf |Industrial PhD position at K2 (Grenoble)}}
 +
 +----
  
 [[https://www.dim-mathinnov.fr/en/phd-fellowships-37.htm|10 PhD positions to be filled in applied maths at DIM Math Innov network]] [[https://www.dim-mathinnov.fr/en/phd-fellowships-37.htm|10 PhD positions to be filled in applied maths at DIM Math Innov network]]
 +
 +----
  
 PhD Position: Sensitivity analysis (CEA Cadarache) {{ :jobs2018:calibration_thesis_proposal_final.pdf |(in English)}} | {{ :jobs2018:sujet_de_these_calibration-final.pdf | (en français)}} PhD Position: Sensitivity analysis (CEA Cadarache) {{ :jobs2018:calibration_thesis_proposal_final.pdf |(in English)}} | {{ :jobs2018:sujet_de_these_calibration-final.pdf | (en français)}}
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 Arbogast, P.,  O. Pannekoucke, L. Raynaud, R. Lalanne and E. Mémin, 2016 : Object‐oriented processing of CRM precipitation forecasts by stochastic filtering. Quart. J. Roy. Meteor. Soc. Arbogast, P.,  O. Pannekoucke, L. Raynaud, R. Lalanne and E. Mémin, 2016 : Object‐oriented processing of CRM precipitation forecasts by stochastic filtering. Quart. J. Roy. Meteor. Soc.
 Raynaud, L., and F. Bouttier, 2016: Comparison of initial perturbation methods for ensemble prediction at convective scale. Quart. J. Roy. Meteor. Soc., 142, 854-866. Raynaud, L., and F. Bouttier, 2016: Comparison of initial perturbation methods for ensemble prediction at convective scale. Quart. J. Roy. Meteor. Soc., 142, 854-866.
----- 
----- 
-**July 2017** 
----- 
----- 
-PhD Position at LIPhy, Grenoble Alpes University (France) and College of Engineering, 
-Swansea University (UK) (Physics/Applied Mathematics). 
- 
-[[http://www-liphy.ujf-grenoble.fr/pagesperso/ismail/researchFiles/thesisProposalSwansea.pdf|Studying the visco-elastic behaviour of lymph through experimental and computational micro-fluidics]] 
----- 
-{{ :jobs2016:phd-covariates.pdf |PhD Position at Troyes: Aging with covariates, estimation and prediction}} 
- 
- 
-{{ :jobs2016:sujet-the_se-maint.pdf |Thèse à Troyes : POlitiques de Maintenance adaptatives pour un 
-système Multi-composants évoluant dans un Environnement Stressant}} 
----- 
-{{ :jobs2016:these-robardetsavinien.pdf|PhD position at LIRIS and Data R&D Institute at EMLyon 
-Business School}} 
----- 
- 
-PhD Proposal : 
-String embeddings for large-scale machine learning in genomics 
- 
-Description: 
- 
-The cost of DNA sequencing has been divided by 100,000 in the last 10 years [1]. It is now so cheap that it has quickly become a routine technique to characterize the genomic content of biological samples with numerous applications in health [2], food or energy [3].  The output of a typical DNA sequencing experiment is a set of billions of short sequences, called reads, of lengths 100~300 in the {A,C,G,T} alphabet ; these billions of reads are then automatically processed and analyzed by computers to get some biological information such as the presence of particular bacterial species in a sample, or of a specific mutation in a cancer. 
- 
-As the throughput of DNA sequencing continues to increase at a fast rate, the major bottleneck in many applications involving DNA sequencing is quickly becoming computational. The goal of this PhD project is to advance the state-of-the-art and propose new solutions for storing and analyzing efficiently the billions of reads produced by each experiment. 
- 
-More precisely, we will focus on two important applications of DNA sequencing : 
--       metagenomics, where the goal is to assign each read to a bacterial species in order to quantify the species that may be present in the sample analyzed ; 
--       RNA-seq, where the goal is to assign each read to a gene, in order to quantify the level of expression of all genes in the sample analyzed. 
-The basic problem to be solved in both applications is to assign each read to one among a set of known, longer target sequences (bacterial genomes or gene sequences). Standard techniques to solve that problem try to align each read to each target, using tools such as BLAST [4], BWA [5] or BOWTIE [6]. However, the computational cost of these techniques becomes prohibitive with current large sequence datasets, and faster alternative have been proposed recently. In particular, the problem can be reformulated as a supervised multiclass classification problem and solved by machine learning techniques such as naive Bayes [7] or support vector machines (SVM) [8]. We recently showed that large-scale machine learning techniques are competitive in accuracy and much better in computational cost that alignment-based methods for metagenomics applications [9]. 
- 
-The standard approach to solve the machine learning formulation is to represent each read as a fixed-length vector and then to train a linear classifier. A typical representation is to count the number of occurrences of each k-mer in a read, and to store these counts in a 4^k – dimensional vector, where k is an integer between 8 and 15. Recently, different representations using gapped k-mers and locality-sensitivity hashing (LSH) have been proposed and led to promising results [10], suggesting that there exists room for improvement in the way we represent reads as vectors for large-scale machine learning. 
- 
-In this context the PhD candidate will investigate and propose new ways to represent DNA sequencing reads, that would lead to both (i) a compact representation for efficient storage and fast processing, and (ii) good performance in read classification for metagenomics and RNA-seq applications. Techniques to be investigated will include, in particular : 
--       Random features to approximate string kernels [11,12] 
--       LSH-based representations, including minHash [13] 
--       Deep learning-based representations, including convolutional [14] and recurrent neural networks 
- 
- 
-Application: 
- 
-PhD supervised by Jean-Philippe Vert (MINES ParisTech / Institut Curie / ENS Paris) : 
-http://members.cbio.mines-paristech.fr/~jvert/ 
- 
-PhD fellowship of MINES ParisTech (about 1,690 €/month, net salary) 
- 
-The ideal candidate should have a background in statistical machine learning, and a keen interest in biological applications (but no prior background in biology is needed). 
- 
-To apply : send CV, transcripts and contact informations of two persons I could reach for recommendation to jean-philippe.vert@mines-paristech.fr before July 6, 2017. 
- 
-Do not hesitate to reach him by email if you have any question. 
- 
- 
-References: 
-  [1] https://www.genome.gov/sequencingcostsdata/ 
-  [2] https://cancergenome.nih.gov 
-  [3] http://www.hydrocarbonmetagenomics.com 
-  [4] Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. Journal of molecular biology, 215(3), 403-410. 
-  [5] Li, H. (2013). Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint arXiv:1303.3997. 
-  [6] Langmead, B., & Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nature methods, 9(4), 357-359. 
-  [7] Qiong Wang, George M Garrity, James M Tiedje, and James R Cole (2007). Naive bayesian classifier for rapid assignment of rrna sequences into the new bacterial taxonomy. Applied and environmental microbiology, 73(16):5261–5267. 
-  [8] Kaustubh R Patil, Peter Haider, Phillip B Pope, Peter J Turnbaugh, Mark Morrison, Tobias Scheffer, and Alice C McHardy (2011). Taxonomic metagenome sequence assignment with structured output models. Nature methods, 8(3):191–192. 
-  [9] Vervier K., Mahé, P.,Tournoud, M., Veyrieras, J.-B., and Vert, J.-P. (2016). Large-scale machine learning for metagenomics sequence classification. Bioinformatics, 32(7) :1023-1032. 
-  [10] Luo, Y., Yu, Y, Zeng, J., Berger, B. and Peng, J. (2017) Metagenomic binning through low density hashing. biRxiv 133116. 
-  [11] Rahimi, and Recht, B. (2007) Random features for large-scale machine learning. In NIPS 2007. 
-  [12] Mourragui, S. (2017). Random projections for large-scale metagenomics classification. Internship report, MINES ParisTech. 
-  [13] Indyk, P. and Motwani, R. (1998). Approximate nearest neighbor: Towards removing the curse of dimensionality. In Proceedings of the Symposium on Theory of Computing. 
-  [14] Zhang, X., Zhao, J., and LeCun, Y. (2016). Character-level Convolutional Networks for Text Classification. arXiv 1509:01626. 
- 
----- 
-Société Orange : Proposition de sujets de thèse 
- 
-  
- 
- 
-Expéditeur: <regine.angoujard@orange.com> 
-Date: 13 juin 2017 à 08:35:23 UTC+2 
-Destinataire: FAINÉANT Virginie IMT/OLR <virginie.faineant@orange.com> 
-Cc: ANGOUJARD MIET Régine IMT/OLR <regine.angoujard@orange.com> 
-Objet: Orange, votre partenaire pour les thèses de vos étudiant(e)s 
- 
-Bonjour, 
- 
-Nous avons le plaisir de vous informer que nous avons sélectionné votre établissement pour proposer nos sujets de thèses 2017 en priorité à vos étudiant(e)s. 
- 
-Depuis de nombreuses années, Orange recrute une quarantaine de doctorants sur des sujets touchant à la fois aux sciences et techniques relatives aux réseaux, aux plateformes de services, aux services et aux usages. Nous souhaitons renforcer la proportion de doctorants issus des cursus de recrutement prioritaires pour Orange, et diversifier les origines thématiques, en ne nous limitant pas exclusivement aux formations en Télécoms, mais en incluant notamment les domaines de l’énergie, des systèmes, de l’informatique et des mathématiques. 
- 
-Vous trouverez,  en avant-première,  la liste des sujets de thèses que nous avons sélectionnés pour le programme doctoral 2017. 
- 
- 
-Nous vous demandons donc de faire la promotion de ces sujets auprès de vos étudiants, sur la base de l’argumentaire suivant : 
- 
- 
-Orange considère ses doctorants comme des acteurs clés du succès de sa recherche. Ils lui apportent des connaissances scientifiques ou technologiques et des  pistes d’innovation nouvelles, considérées comme une priorité stratégique par le Groupe. 
- 
-Pendant leurs trois années de thèse, nos doctorants, recrutés en CDD,  participent avec leurs encadrants à une grande variété de projets, internes au Groupe ou coopératifs (ANR, projets européens…). Ils peuvent bénéficier d’une formation spécifique de préparation à la vie active financée par le Groupe auprès de l’ABG (Association Bernard Gregory). 
- 
-La politique doctorale du Groupe permet à nos doctorants de construire des liens et des synergies efficaces avec le monde de la recherche publique (Institut Mines-Télécom, INRIA …), et aussi d’avoir une première expérience très significative du travail en entreprise, ce qui ouvre des perspectives de carrière privilégiées à la fois dans le domaine de la Recherche (publique ou privée) et aussi dans les Entreprises, et pas uniquement dans leurs laboratoires de recherche. 
- 
- 
-Pour plus de détails, ces offres sont actuellement consultables sur [[https://orange.jobs/site/fr-theses/offres-d-emploi.htm|Orange.jobs thèses]] , les candidats intéressés devront obligatoirement postuler en ligne. 
- 
- 
-Je me tiens à votre disposition pour tous renseignements complémentaires, 
- 
-Cordialement, 
- 
- 
-Régine Angoujard Miet 
-Gestion du programme doctoral 
-ORANGE/IMT/OLR/DOP/PRA   
-Fixe : +33 1 57 39 93 31 
-regine.angoujard@orange.com 
- 
- 
----- 
- 
-PhD Scholarship available (UNSW Sydney, Australia) 
- 
-Deadline: 21 July, 2017 
- 
-Details here: http://web.maths.unsw.edu.au/~lafaye/#opportunities 
- 
-The UNSW Scientia Ph.D. Scholarship Scheme is the most prestigious and generous scholarship scheme at UNSW. It aims to attract the best and brightest people into strategic research areas. Awardees receive a $50,000 scholarship package for four years, comprising a $40,000 per annum tax-free stipend and a travel and development support package of up to $10,000 per annum. International students also receive a tuition fee scholarship. In addition to this scholarship package, scholars are provided with access to a range of development opportunities across research, teaching and learning and leadership and engagement. 
- 
-The funded project aims to develop new tools and insights for insurer risk management by combining modern statistical learning (‘data analytics’, ‘big data’, ‘predictive analytics’) techniques with actuarial risk theory. The findings will allow for accurate and equitable rating and measurement of risks, and ultimately contribute to sustainable and equitable protection for policyholders. For equity and stability, insurers must be able to assess their risks accurately. Nowadays, they have access to an increasing number of data sources of very different types, and in finer and finer detail. This interdisciplinary project is concerned with 21st century estimation of insurance risks, and proposes to deal with all of the four V’s of big data: volume, velocity, variety and veracity. The focus will be on the extension of recent statistical analytics including in particular deep learning. Insights developed with this analysis will be further incorporated into concepts from actuarial risk theory. 
- 
-The supervisory team will comprise Benjamin Avanzi and Bernard Wong (both Associate Professor at the Business School, Risk and Actuarial Studies) and Pierre Lafaye de Micheaux (Senior Lecturer, School of Mathematics and Statistics). 
- 
-The candidate must have a strong background in statistics/mathematics and good programming skills (preferably in R and C/C++; some experience with Linux would be an asset). 
- 
-If you are interested, please contact either of us for more details, joining a recent CV and a copy of your academic transcripts. 
----- 
- 
----- 
----- 
-**June 2017** 
- 
----- 
- 
-[[https://www.inserm-u1000.u-psud.fr/wp-content/uploads/2017/06/Inserm_job-IT-English_U1000_data-scientist.pdf|Data Scientist at Inserm Orsay & Paris]] 
- 
----- 
-OFFRE DE BOURSE D’ETUDE FRANCAISE 2017-2018 
- 
-  * Unordered List ItemAfin de promouvoir les compétences des ressources humaines des pays en développement et de favoriser la compréhension, l’amitié entre les nations et le peuple Français, la Commission Nationale Française (CNF) pour l'UNESCO en accord avec le Secrétariat d’Etat Français à l’Education  (SEFE) met 100 (cent) bourses d’études à la disposition des étudiants étrangers désireux d’effectuer un séjour d’étude ou de recherche en France pour l’année académique 2017/2018. 
-  * Un formulaire de présélection a été conçu en PDF et mis à la disposition de tous les candidats afin de constituer le dossier de candidature. Merci de bien vouloir faire une demande dudit formulaire auprès Secrétariat d’Etat Français à l’Education  (SEFE) en envoyant une lettre de motivation mentionnant votre pays d’origine afin d’être orienté pour le dépôt de votre dossier de candidature 
-  * Courriel du département de bourse d’étude : <sefe.gov.fr@diplomats.com> 
- 
-  
- 
-Secrétariat d’Etat Français à l’Education  (SEFE)  
- 
----- 
- 
-PhD proposal / Thèse CIFRE : data-science for chip manufacturing, at STMicroelectronics 
- 
-  * STMicroelectronics (Crolles) et le Laboratoire G-SCOP (Grenoble) propose une thèse CIFRE : 
-  * Mission : Science des données pour l'amélioration des processus de conception et de fabrication des puces de silicium. 
-  * Profil recherché : compétences en analyse de données, statistiques et programmation; intérêt pour les applications industrielles et l'évolution dans un environnement complexe et pluridisciplinaire 
- 
-Voir aussi [[http://st.mycvthequehq.com/offre-fr-e3c12b79d162.html]] 
- 
-Contacts : <pierre.lemaire@grenoble-inp.fr>, <bertrand.le-gratiet@st.com> 
- 
----- 
-** 7 prestigious PhD Student Positions with the REVOLVE project ** 
- 
-{{ :jobs2016:7phd_positions_revolve.pdf |}} 
----- 
-** PhD position: Privacy risk assessment and algorithms for matching and enriching  personal and professional profiles across social networks** 
- 
-We may have a PhD subject to propose on "Privacy risk assessment and algorithms for matching and enriching  personal and professional profiles across social networks" (see short description below (*)). 
- 
-This PhD work would be done in collaboration between the SLIDE team of the LIG (Laboratoire d'Informatique de Grenoble) and the talent.io company in Paris, and funded by a CIFRE convention. 
-The candidate would be employed by the company for 3 years to conduct his PhD work, co-supervised by Oana Goga and Marie-Christine Rousset (who are researchers at  LIG). 
- 
-Let us now if you are interested by the subject and the industrial setting of this PhD work. If this is the case, Nicolas Meunier CEO of the talent.io company will get in touch with you for further discussions. 
- 
- 
-(*) Short description: 
-================== 
-talent.io is a linking platform for helping developers to find the best jobs for them according to their profile, and tech companies to find the best developers for their needs. The distinguishing point of this platform is to push candidate profiles to companies that have registered to the platform. It is therefore very important that the pushed profiles are the most complete and precise as possible so the right candidates are contacted by the right companies as fast as possible. At the moment, the profiles are mainly built manually, first by the candidates when they sign up and then enriched by talent.io staff based on phone interviews with candidates. For candidates providing their linkedin profile, some profile items are automatically extracted from their linkedin profile. The goal of the project is to make the profile construction more automatic by taking benefit of existing profiles that can be found across social or professional networks. In particular, talent.io has already collected 5.5 millions of github profiles. It is also registered to services that provide on demand social network profiles matching with a keyword query. The first step of the project will be dedicated to collect a sample of profiles data and to put them in an appropriate format for applying some of the state-of-the-art profile matching algorithms. The second step will consist in comparing the performance and the results of the different chosen profile matching algorithms on these profile data. In particular, the number of false positive matches is an important criteria for assesssing the quality of a profile matching algorithm. Finally, potential solutions for exploiting aggregated profiles should be proposed in order to choose the profiles to push or to recommend to target companies. 
- 
- 
----- 
----- 
-**May 2017** 
- 
----- 
-**Permanent Research Engineer position in medical image processing** 
- 
-Starting date : September 2017 (could be adapted to availability) 
- 
-Application: see [[http://mathhatt.free.fr/posteIR.pdf|http://mathhatt.free.fr/posteIR.pdf]] 
- 
-Contact Dr Catherine Cheze Le Rest (Catherine.cheze-le-rest@chu-poitiers.fr) for more information. 
- Interested candidate should send curriculum vitae, complete list of past research topics and publications, a motivation letter, and two references to Dr Catherine Cheze Le Rest (Catherine.cheze-le-rest@chu-poitiers.fr) . Review of applications will begin immediately and continue until the position is filled.  
- 
- 
- 
----- 
----- 
-=== PhD position at Université Paris-Sud === 
- 
-http://www.adum.fr/as/ed/voirproposition.pl?site=PSaclay&matricule_prop=15681 
- 
-=== PhD position within Institut Mines Telecom (Telecom SudParis) === 
- 
-__**PhD Title :**__ Proactive Mobility, Naming and Caching in future Complex 5G Network Services: Modeling, Simulation and Experimentation. 
- 
- 
-__**Keywords:**__ ICN, IoT, 5G, complex graphs, proactive models 
- 
- 
-__**Profile and skills required**__ 
- 
- 
-  * A Master's degree or an engineer Computer Science and /or Applied Mathematics. 
-  * Good understanding of the fundamental of and network science in general 
-  * Knowledge in optimization theory, machine learning theory, graph theory, stochastic processes, Bayesian networks and/or Game theory are highly desirable. 
-  * Programming potential in various languages (Python, C/C ++,Matlab, Java,...etc). 
-  * A good level of English. 
-  * A strong curiosity in research interdisciplinary. 
-  * English speaking and writing. 
- 
- 
-__**Director and supervisor: Prof. H. Afifi and Prof. H. Moungla**__ 
- 
- 
-__**Location: **__ 
- 
-Télécom-SudParis / Université Paris Saclay 
- 
-CNRS - Telecom SudParis 
- 
-CEA Saclay Nano-Innov 
- 
-Avenue de la Vauve - Bat 861 
- 
-91191 Gif-sur-Yvette, France 
- 
- 
-The PhD details are on the website (link below) of the Paris-Saclay University, and applications will have to be sent to:  <hassine.moungla@telecom-sudparis.eu>  &  <hossam.afifi@telecom-sudparis.eu>  
- 
- 
-__**And to submit on the site of adum of the Paris Saclay University before May 12, 2017.**__ 
- 
- 
-http://www.adum.fr/as/ed/voirproposition.pl?site=PSaclay&matricule_prop=16320 
- 
- 
-The PhD description will be in the pdf file (EN version) with the same link at the bottom of the web page.  
- 
-This position is part of Carnot 2017 call, for October 2017. 
- 
-=== PhD position at LMDC/IMT:=== 
-{{ :jobs2016:sujet_duprat-de_larrard-dauxois_2017.pdf |Modélisation stochastique des phénomènes de dégradation des ouvrages en béton}} 
- 
----- 
----- 
-**April 2017** 
----- 
----- 
- 
----- 
-=== 3-Year PhD position at IRSTEA:=== 
-http://www.irstea.fr/sites/default/files/phd_real-time_dynamic_q-h_models-1.pdf 
- 
----- 
-=== 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 
- 
-== Context == 
- 
- 
-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. 
- 
-== Candidate == 
- 
- 
-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. 
- 
-== Salary == 
- 
- 
-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/) 
- 
-== Bibliography == 
- 
- 
-[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). 
- 
- 
- 
- 
- 
----- 
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-**March 2017** 
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- 
- 
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- 
-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 
- 
-[[https://www.inria.fr/institut/recrutement-metiers/offres/doctorants/doctorants/(view)/details.html?id=PGTFK026203F3VBQB6G68LONZ&LOV5=4509&LG=FR&Resultsperpage=20&nPostingID=11431&nPostingTargetID=18044&option=52&sort=DESC&nDepartmentID=28|https://www.inria.fr/institut/recrutement-metiers/offres/doctorants/doctorants/(view)/details.html?id=PGTFK026203F3VBQB6G68LONZ&LOV5=4509&LG=FR&Resultsperpage=20&nPostingID=11431&nPostingTargetID=18044&option=52&sort=DESC&nDepartmentID=28]] 
- 
-where the students can apply. 
- 
- 
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-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 : 
- 
-http://www.laurent-duval.eu/lcd-2017-intern-sparse-regression-dim-reduction.html 
-  
- 
----- 
-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 
- 
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- 
-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 . 
- 
  
joboffers.1560437089.txt.gz · Last modified: 2019/06/13 16:44 by jbdurand
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