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joboffers [2019/07/22 09:31]
jbdurand
joboffers [2019/07/22 09:33] (current)
jbdurand
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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.
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-**July 2017** 
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-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]] 
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-{{ :​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}} 
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-{{ :​jobs2016:​these-robardetsavinien.pdf|PhD position at LIRIS and Data R&D Institute at EMLyon 
-Business School}} 
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- 
-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 
- 
- 
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- 
-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. 
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- 
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-**June 2017** 
- 
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- 
-[[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]] 
- 
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-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) ​ 
- 
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- 
-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>​ 
- 
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-** 7 prestigious PhD Student Positions with the REVOLVE project ** 
- 
-{{ :​jobs2016:​7phd_positions_revolve.pdf |}} 
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-** 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. 
- 
  
joboffers.txt · Last modified: 2019/07/22 09:33 by jbdurand
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