Random November 2019

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Seminatio Pierre Patie @ UniTo - 5 Dicembre
by Bruno Toaldo 25 Nov '19

25 Nov '19

********************************************************************* SEMINARI DI PROBABILITÀ E STATISTICA MATEMATICA DIPARTIMENTO DI MATEMATICA "G. PEANO" UNIVERSITÀ DEGLI STUDI DI TORINO ********************************************************************* Giovedì 5 Dicembre 2019 alle ore 14:00 nell'aula Magna del Dipartimento di Matematica "G. Peano" dell'Università degli Studi di Torino, Via Carlo Alberto 10, Il Prof. *Pierre Patie* (Cornell University, Ithaca, NY, USA) terrà un seminario dal titolo *Completely monotone intertwining relations* Abstract: In this talk, we introduce the concept of completely monotone intertwining relations as a strengthening of usual intertwining relations between Markov semi-groups. We proceed by providing some interesting applications of this new idea which includes the characterization of ergodic constants and hypercontractivity estimates for non-self-adjoint semigroups. We illustrate these results by presenting several examples that have emerged from the recent literature: discrete-to-continuous interacting particle models, degenerate hypoelliptic Ornstein-Uhlenbeck processes, and diffusion-to-jump Jacobi processes. This talk is based on a joint a work with L. Miclo (IMT Toulouse). Tutti gli interessati sono invitati a partecipare.

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First Announcement Conference SPDE&A Levico 2020
by Stefano Bonaccorsi 25 Nov '19

25 Nov '19

First Announcement of the Conference on Stochastic Partial Differential Equations & Applications - XI edition We are pleased to inform you that the "Conference on Stochastic Partial Differential Equations & Applications" will be held from June 1st to June 5th, 2020 in Levico Terme (Trento, Italy). This is the XI-th edition of a series of conferences on SPDEs which, during the years, has become one of the most popular occasions for the community to meet and discuss the latest developments. This year we have, for the first time, 4 mini-courses given by (1) Francesco Caravenna (Italy) (2) Martin Hairer (UK) (3) Martina Hofmanova (Germany) (4) Nikolay Tzvetkov (France) All the researchers in the field are invited to participate. We especially invite young researchers (at Ph.D. or Post-doctorate level) to attend the conference and give a short presentation in dedicated sessions we aim to organize. We are trying to secure funds to support their expenses. Any further information can be found in the website of the conference: https://spdelevico2020.wordpress.com/ Best regards, Stefano Bonaccorsi, Sandra Cerrai, Franco Flandoli, Lorenzo Zambotti (organizing committee)

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Seminario RYDER
by Pierpaolo De Blasi 22 Nov '19

22 Nov '19

STATISTICS SEMINARS @ COLLEGIO CARLO ALBERTO Venerdì 29 Novembre 2019, alle ore 12:00, presso il nuovo edificio del Collegio Carlo Alberto, in Piazza Arbarello 8, Torino, si terrà il seguente seminario: ------------------------------------------------ Robin RYDER (Université Paris Dauphine) A BAYESIAN NON-PARAMETRIC METHODOLOGY FOR INFERRING GRAMMAR COMPLEXITY Based on a set of strings from a language, we wish to infer the complexity of the underlying grammar. To this end, we develop a methodology to choose between two classes of formal grammars in the Chomsky hierarchy: simple regular grammars and more complex context-free grammars. To do so, we introduce a probabilistic context-free grammar model in the form of a Hierarchical Dirichlet Process over rules expressed in Greibach Normal Form. In comparison to other representations, this has the advantage of nesting the regular class within the context-free class. We consider model comparison both by exploiting this nesting, and with Bayes’ factors. The model is fit using a Sequential Monte Carlo method, implemented in the Birch probabilistic programming language. We apply this methodology to data collected from primates, for which the complexity of the grammar is a key question. (joint work with L. Murray, J. Rousseau and A. Thin) ------------------------------------------------ Tutti gli interessati sono invitati a partecipare. Il seminario è organizzato dalla "de Castro" Statistics Initiative (www.carloalberto.org/stats <http://www.carloalberto.org/stats>) in collaborazione con il Collegio Carlo Alberto. Cordiali saluti, Pierpaolo De Blasi --- University of Torino & Collegio Carlo Alberto https://sites.google.com/a/carloalberto.org/pdeblasi/ <https://sites.google.com/a/carloalberto.org/pdeblasi/>

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Mini-corsi a Strasburgo per studenti magistrali
by Dario Trevisan 21 Nov '19

21 Nov '19

Salve, segnalo questa iniziativa di mini-corsi di probabilità che si terranno a Strasburgo nella settimana 20-24 Gennaio 2020, http://irma.math.unistra.fr/stochastique/mc2020/ rivolta a studenti delle lauree magistrali francesi (M1) -- principalmente per pubblicizzare il loro M2 -- ma aperta alla presenza di studenti equivalenti italiani (primo anno di magistrale). Mi hanno comunicato che sono ancora a disposizione alcuni fondi per pagare/rimborsare viaggio e alloggio, e di fare pubblicità anche in Italia. Se siete a conoscenza di qualche studente particolarmente capace e interessato, magari che parli un po' di francese, invitatelo pure a mettersi direttamente in contatto con gli organizzatori (N. Juillet, X. Zeng). Saluti, Dario -- Dario Trevisan, Università degli Studi di Pisa, Dipartimento di Matematica, Largo Bruno Pontecorvo 5, 56127 - Pisa (PI) Italy telephone: (+39) 050 2213832 mobile: (+39) 331 2899761 Skype: dario-trevisan e-mail: dario.trevisan AT unipi.it webpage: http://people.dm.unipi.it/trevisan/

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ECSO - CMS 2020 first Call for Papers
by Martina Nardon 20 Nov '19

20 Nov '19

* Apologies for cross-posting First Announcement and Call for Papers Joint EUROPEAN CONFERENCE ON STOCHASTIC OPTIMIZATION and COMPUTATIONAL MANAGEMENT SCIENCE Conference 1-3 July 2020, Venice, Italy We are delighted to invite you to Venice, Italy, 1-3 July 2020, at the Department of Economics, Ca’ Foscari University. ECSO - CMS 2020 is jointly organized by the Department of Economics of Ca’ Foscari University of Venice, the CMS Journal and the EURO Working Group on Stochastic Optimization. ECSO 2020 is the 3rd edition of a stream of conferences organized by the EURO Working Group on Stochastic Optimization (EWGSO). The previous editions were held in Paris (2014) and Rome (2017). The scope of the conference is to bring together researchers and professionals in Stochastic Optimization and its applications in different fields spacing from economics and finance to supply chain, logistics, etc. CMS 2020 is the 17th edition of an annual meeting associated with the journal of Computational Management Science published by Springer. The aim of the conference is to provide a forum for theoreticians and practitioners from academia and industry to exchange knowledge, ideas and results in a broad range of topics relevant to the theory and practice of computational methods in management science. This joint event will provide a forum for fruitful discussions and interactions among researchers and professionals from industry and institutional sectors on decision making under uncertainty in a complex world. The conference will be within the scopes of both CMS and EWGSO and, in particular, it will focus on models, methods and computational tools in stochastic, robust and distributionally robust optimization and on computational aspects of management science with emphasis on risk management, valuation problems, measurement applications. Traditional fields of application, such as finance, energy, water management, logistics, supply chain management, and emerging ones, such as healthcare, climate risk and sustainable development, will be included. Venue: Department of Economics, Ca’ Foscari University of Venice San Giobbe Campus – Cannaregio 873, 30121 Venice, Italy Webpage: www.unive.it/ecsocms2020 IMPORTANT DATES Abstract submission: March 1, 2020 Notification of acceptance: April 1, 2020 Early registration: April 15, 2020 A Best Student Paper Prize of 600 euro will be awarded. Papers should be nominated via e-mail by the students’ supervisors (ecsocms2020(a)unive.it). Deadline for the submissions to the prize is May 15. The program will include a devoted session for presenting the best papers to compete for the prize, such that the jury could make the final choice. The paper does not have to be published. The papers should be principally authored by the student, but co-authors are permitted as long as their contributions are clarified. Only registered participants’ papers will be considered for the prize. Jury for the Student Best Paper Prize: Stein-Erik Fleten (NTNU Norwegian University of Science and Technology), Milos Kopa (Charles University of Prague), Francesca Maggioni (University of Bergamo), Ruediger Schultz (University Duisburg-Essen). INVITED SPEAKERS DARINKA DENTCHEVA, Stevens Institute of Technology (USA) DAVID MORTON, Northwestern University (USA) GAH-YI BAN, London Business School (UK) DANIEL KUHN, École polytechnique fédérale de Lausanne (CH) GIORGIO CONSIGLI, Università di Bergamo (I) Conference Secretariat: ecsocms2020(a)unive.it Conference hashtag: #ecsocms2020 Best Regards, Diana Barro, Stein-Erik Fleten and Martina Nardon Organizing and Program Committee Chairs -------------------------------------- Dr. Martina Nardon Dipartimento di Economia Università Ca' Foscari Venezia San Giobbe - Cannaregio, 873 30121 Venezia, Italy tel. +39 041 234 7413 --------------------------------------

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Fwd: [ST-NET] Edinburgh Lectureship/Readership in Probability and Stochastic Analysis
by Mario Maurelli 19 Nov '19

19 Nov '19

---------- Forwarded message --------- Da: Martin Keller-Ressel <st-net(a)mailbox.tu-dresden.de> Date: lun 21 ott 2019 alle ore 18:46 Subject: [ST-NET] Edinburgh Lectureship/Readership in Probability and Stochastic Analysis To: st-net(a)zib.de <st-net(a)zib.de> Von: Goncalo dos Reis @UoE <g.dosreis(a)ed.ac.uk> --- Dear friends, dear colleagues, Permanent position in Probability and Stochastic Analysis Position: Lectureship (assistant prof)/Readership (associate prof) As part of an ambitious long-term plan for the School of Mathematics, the University of Edinburgh is making a substantial number of new appointments in the Mathematical Sciences, which includes expansion into part of the new £40M Bayes Centre. As part of this development, we are recruiting in Probability and Stochastic Analysis (broadly interpreted) to start on 1st June 2020 or around that date by mutual agreement. We expect candidates to have either an established record of first-class research or to be able to demonstrate excellent research potential. Those pursuing interdisciplinary research with links to other fields, for instance, Computing, Engineering, Finance, Physics, or links to industry are also encouraged to apply. In addition to carrying out high-quality research, the successful candidate will contribute to the teaching of undergraduate courses and the teaching and further development of our successful MSc programmes in Mathematical Finance. Salary Scale: £41,526 - £49,553 per annum. Very strong and experienced applicants may be appointed to a Readership, for which the salary is £52,559 - £59,135. Applications close at 5pm (UK time) on 29 November 2019 Interviews will take place in Edinburgh during the week 13-17 January 2020 Informal enquiries may be made to Professor Iain Gordon (Head of School) email hosmaths(a)ed.ac.uk. further details: https://www.vacancies.ed.ac.uk/pls/corehrrecruit/erq_jobspec_version_4.jobs… with kind regards to all Goncalo dos Reis _______________________________________________ St-net mailing list St-net(a)zib.de https://listserv.zib.de/mailman/listinfo/st-net --------------------------------------------------------------------------- Dies ist das ST-NET, der (moderierte) Mail-Verteiler der DMV-Fachgruppe Stochastik e.V. Weitere Informationen finden Sie auf unserer Homepage: http://www.fg-stochastik.de This is ST-NET, the (moderated) mailing list of the DMV-Fachgruppe Stochastik e.V. For further information, please refer to our homepage: http://www.fg-stochastik.de Please note the guidelines for posting at http://www.fg-stochastik.de/beitraege-verschicken.html ---------------------------------------------------------------------------

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Fwd: Probability postdoc @ McGill
by Faggionato, Alessandra 19 Nov '19

19 Nov '19

---------- Messaggio inoltrato ---------- Da: *Louigi Addario-Berry* <louigi(a)gmail.com> Data: sabato 16 novembre 2019 Oggetto: Probability postdoc @ McGill A: "Linan Chen, Prof." <linan.chen(a)mcgill.ca>, Jessica Lin < jessica.lin(a)mcgill.ca>, Elliot Paquette <elliot.paquette(a)gmail.com> Dear colleague, We are advertising a 3-year postdoc in probability at McGill; details appear below. We would be grateful if you could pass along this ad to anyone who may be interested. The application deadline is December 10. Thanks very much, Louigi, Linan, Jessica and Elliot. -- To apply: https://www.mathjobs.org/jobs/jobs/15010 The Department of Mathematics and Statistics at McGill University is advertising a three-year postdoctoral fellowship in probability theory and related fields. The postdoctoral fellow will include a teaching obligation of two courses per year. Candidates must have a doctoral degree in mathematics or a related discipline at the date of appointment, and must have demonstrated the capacity for independent research of excellent quality. The probability group includes four faculty members from the McGill Department of Mathematics and Statistics (Louigi Addario-Berry, Linan Chen, Jessica Lin and Elliot Paquette), as well as several faculty members from other McGill departments and other Montreal universities. The fellowship will include a generous salary, and additional funding will be available to the postdoc for research-related travel. Moreover, postdoctoral fellows, depending on their residency status, may pay a reduced tax rate (via the Québec program "Tax Exemption for Foreign Postdoctoral Trainees"). McGill University is committed to equity and diversity. We welcome and encourage applications from women, Indigenous persons, people with disabilities, members of racialized communities, LGBTQIA+ individuals, and other groups which are currently underrepresented within the mathematical community. Candidates are welcome to signal any leave that affected productivity, or any other circumstances that may have had an effect on their career path. This information will be considered to ensure the equitable assessment of the candidate’s record. McGill seeks to ensure the equitable treatment and full inclusion of people with disabilities by striving for the implementation of universal design principles transversally, across all facets of the University community, and through accommodation policies and procedures. Individuals who anticipate needing accommodations for any part of the application process may contact, in confidence, accessibilityrequest.hr(a)mcgill.ca, or phone at 514-398-2477. Applications should be submitted via MathJobs.org and should include 3 references. All qualified applicants are encouraged to apply; however, in accordance with Canadian immigration requirements, Canadians and permanent residents will be given priority. To ensure full consideration applications must be received by December 10, 2019. -- Information about work permits, healthcare and benefits, parental leave, and other employment-related questions can be found at the following links. https://www.mcgill.ca/gps/postdocs https://www.mcgill.ca/gps/postdocs/international/workpermit https://www.mcgill.ca/gps/postdocs/fellows/healthcare https://www.mcgill.ca/gps/students/graduate-policies- and-regulations/parental-leave -- ************************************************* Prof. Alessandra Faggionato http://www1.mat.uniroma1.it/~faggionato/ Department of Mathematics University "La Sapienza" Piazzale Aldo Moro, 5 00185 - Rome Office 5, Phone (0039) 06 49913252 *************************************************

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Seminar Wadsworth @Bocconi
by Giacomo Zanella 18 Nov '19

18 Nov '19

We announce the following DEC Statistics seminar: Thursday, November 21 12:30 Meeting room 3-e4-sr03, Bocconi University, Via Roentgen 1, 3rd floor Jenny Wadsworth (Lancaster University) Title: Towards higher-dimensional spatial and spatio-temporal extremes Abstract: The past decade has seen a huge effort in modelling the extremes of spatial processes. Significant challenges include the development of models with an appropriate asymptotic justification for the tail; ensuring model assumptions are compatible with the data; and the fitting of these models to (at least reasonably) high-dimensional datasets. I will review basic ideas of modelling spatial extremes, and introduce a more scalable approach via conditioning on a single site being extreme, which can also be applied in the space-time context. Furthermore, the sometimes-undesirable nature of conditioning on an extreme event at a particular location can be circumvented by an importance sampling algorithm to change the conditioning event. As we move towards being able to model extremes at more locations, we must also learn to deal with the complex structures of larger datasets, such as spatial nonstationarity in the extremal dependence. Some approaches to simplify these structures via deformations tailored to extremal dependence will also be discussed. Kind regards, Giacomo Zanella The DEC statistics seminars schedule is available at http://www.unibocconi.eu/statseminar Please note: if you are a guest and you do not have a Bocconi ID Card to access to the Bocconi Buildings, please communicate your participation by sending an email to elisa.picassi(a)unibocconi.it

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Seminar: okada@DISMEQ Department of Statistics and Quantitative Methods, University of Milano-Bicocca
by Fulvia Pennoni 18 Nov '19

18 Nov '19

----------------------------------------------------------------------------------- ** 21/11/2019 h.11.00 /Building U7 4th floor - Seminar Room 4026/ Department of Statistics and Quantitative-Methods, Via Bicocca degli Arcimboldi, 8 - 20126 Milano ----------------------------------------------------------------------------- MULTIDIMENSIONAL SCALING OF ASYMMETRIC RELATIONSHIPS Akinori Okada Professor Emeritus of the Rikkyo University, Tokyo okada(a)rikkyo.ac.jp Torgerson (1952) introduces his multidimensional scaling as the first practical method of representing proximity (similarity, dissimilarity) relationships among objects in a multidimensional space or a configuration. The method is called metric, which means that the observed proximities are assumed to be interval or ratio scaled values. Shepard (1962a, b) and Kruskal (1964a, b) introduced methods which are called nonmetric multidimensional scaling. Nonmetric multidimensional scaling assumes that the observed proximities are ordinal scaled values. This mean that only the information which of two proximities is larger (or smaller) than the other is utilized in the analysis. The nonmetric multidimensional scaling of Kruskal (1964a, b) has been used most frequently in practical applications of multidimensional scaling, and several multidimensional scaling methods based on the similar idea of Kruskal (1964a, b) have been introduced. The multidimensional scaling developed so far assumes that the proximity is symmetric. Several researchers focused their attention on the asymmetry in various sorts of proximity; similarity/dissimilarity judgment, same/different judgment, reaction time, social and psychological distance, and so on. It is meaningful to develop asymmetric multidimensional scaling which can represent asymmetric proximity relationships among objects in a configuration. Various kinds of procedures to analyze an asymmetric proximity matrix were invented. We concentrate on the multidimensional scaling which is based on the distance among points, each representing an object, in a multidimensional space (distance model). The asymmetric multidimensional scaling by Okada and Imaizumi (1987) represents asymmetric proximity relationships among objects by the interpoint distance in a multidimensional configuration. The distance between two points is symmetric. It is difficult to represent asymmetric relationships simply by the interpoint distance, because the distance itself is symmetric. The model of Okada and Imaizumi (1987), which is called Distance-radius model, represents an object by a point and a circle [two-dimensional model] (sphere [three-dimensional] or hypersphere [larger than three-dimensional]) centered at the point in a multidimensional space. Several models were derived from the model. Multidimensional scaling mentioned above, regardless of metric or nonmetric and of being able to represent asymmetry or not, analyzes one proximity matrix (object x object) or one-mode two-way proximities (Carroll and Arabie, 1980). To analyze a set of proximity matrices each comes from a source (object x object x source) or two-mode three-way proximities, INDSCAL was developed by Carroll and Chang (1970). INDSCAL can represent relationships among objects and how each source (individual) has different relationships among objects each other. While INDSCAL can represents differences among sources, it cannot represent the asymmetry in relationships among objects of each source. The model of Okada and Imaizumi (1997) is an extension of the Distance-radius model which can analyze a set of asymmetric proximity matrices or two-mode three-way asymmetric proximities. The asymmetric multidimensional scaling of Okada and Imaizumi (1997) can represents asymmetric relationships among objects, and how each source has different symmetric and asymmetric relationships among objects each other as well. Several models, which are less restricted or more restricted, have been derived from Okada and Imaizumi (1997). To analyze one-mode two-way asymmetric proximities, there is another asymmetric multidimensional scaling by Okada (2011) and Okada and Tsurumi (2012) which is based on the singular value decomposition. The asymmetric multidimensional scaling has been utilized to analyze mainly the data from marketing research or brand switching. Several models and procedures have been derived by extending the model of Okada (2011) which can, for example, evaluate newly introduced brands, diagnose brands, tell how to increase the dominance of a brand in the brand switching... Another sort of procedure to analyze one-mode two-way asymmetric proximities is asymmetric cluster analysis, which can represent asymmetric proximity relationships among objects by forming clusters (Okada & Iwamoto, 1966; Okada & Yokoyama, 2015). It is important and meaningful to develop not only multidimensional scaling but also cluster analysis as well so that we can use two different kinds of methods to analyze the same proximity data (Arabie & Hubert, 1994). There are quite a few number of applications of asymmetric multidimensional scaling and cluster analysis. While these applications are not mentioned here because of the space limitation, some of them will be mentioned in the talk. The present abstract deals only with researches of the author and his colleagues. Bove and Okada (2018) gives a review which covers a wide range of asymmetric multidimensional scaling and asymmetric cluster analysis in detail. Some books mention the analysis of asymmetric relationships. Borg and Groenen (2005) has a chapter of asymmetric multidimensional scaling. Coxon and Coxon (2001) refers to asymmetric multidimensional scaling. **/References/// Arabie, P. & Hubert, L. (1994). Cluster analysis in marketing research. In Bagozzi, R. F. (Ed.), A/dvanced methods marketing research/. pp. 160-199. Cambridge, MA: Blackwell. Bove, G., & Okada, A. (2018). Methods for the analysis of asymmetric pairwise relationships. Advances in Data Analysis and Classification, *12*, 5-31. Borg, I., & Groenen, P.J.F. (2005). /Modern multidimensional scaling: Theory and applications/(2nd ed.). New York: Springer. Carroll, J. D., & Arabie, P. (1980). Multidimensional Scaling. In M. R. Rosenzweig & L. W. Porter (Eds.), /Annual Review of Psychology/, *31*, 607-649. Carroll, J. D. & Chang, J. J. (1970). Analysis of individual differences individual differences in multidimensional scaling via an /N/-way generalization of “Eckart-Young” decomposition. /Psychometrika/, /35/, 283-319. Coxon, T.F., & Coxon, M.A.A. (2001). /Multidimensional scaling/ (2nd ed.). Boca Raton, FLChapman&Hall/CRC. Kruskal, J. B. (1964a). Nonmetric multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. /Psychometrika/, /29/, 1-27. Kruskal, J. B. (1964b). Nonmetric multidimensional scaling: A numerical method. /Psychometrika/, *29,* 115-129. Okada, A. (2011). Analyzing social affinity for foreigners by spectral decomposition: Asymmetric multidimensional scaling of EASS 2008 Data [Supekutoru bunkai niyoru gaikokujin ni taisuru shinkinkan no bunseki: EASS 2008 no deta wo mochiita hitaishou tajigenshakudokoueihou no ouyou]. In JGSS Research Center, (Ed.), /JGSS Research Series/, /11/. (pp. 119-128). Osaka: Osaka University of Commerce. Okada, A., & Imaizumi, T. (1987). Nonmetric multidimensional scaling of asymmetric proximities. /Behaviormetrika, /*21*/,/ 81-96. Okada, A., & Imaizumi, T. (1997). Asymmetric multidimensional scaling of two-mode three-way proximities. /Journal of Classification/,*14, *195-224. Okada, A., & Iwamoto, T. (1996). University enrollment flow among the Japanese prefectures: A comparison before and after the Joint First Stage Achievement Test by asymmetric cluster analysis. /Behaviormetrika/, *23*, 169-185. Okada, A., & Tsurumi, H. (2012). Asymmetric multidimensional scaling of brand switching among margarine brands./Behaviormetrika/, *39*, 111-126. Okada, A., & Yokoyama, S. (2015). Asymmetric CLUster analysis based on SKEW-Symmetry: ACLUSKEW. In I. Morlini, M. Tommaso, & M. Vichi, (Eds.), /Advance in statistical models for data analysis/.//191-199. Heidelberg, Germany: Springer-Verlag. Shepard, R. N. (1963a). The analysis of proximities: Multidimensional scaling with unknown distance function. I. /Psychometrika/, *27*, 125-140. Shepard, R. N. (1963b). The analysis of proximities: Multidimensional scaling with unknown distance function. II. /Psychometrika/, *27,* 219-246. Torgerson, W. T. (1952). Multidimensional scaling: I. Theory and method. /Psychometrika/, *17*, 401-419. ** /For more info:/***//**/fulvia.pennoni@unimib.it <mailto:fulvia.pennoni@unimib.it>/* -- Fulvia Pennoni Ph.D. Department of Statistics and Quantitative Methods University of Milano-Bicocca https://sites.google.com/view/fulviapennoni

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Fwd: PhD fellowship in mathematical and computational methods for time series analysis
by Giovanni Peccati 17 Nov '19

17 Nov '19

Dear Colleagues, I am forwarding a message from K. Ebrahimli-Fard, advertising a prestigious doctoral position in Trondheim. All my best, Giovanni ---------- Forwarded message --------- From: Kurusch Ebrahimi-Fard <kurusch.ebrahimi-fard(a)ntnu.no> Date: Wed, 13 Nov 2019 at 18:19 Subject: PhD fellowship in mathematical and computational methods for time series analysis To: giovanni.peccati(a)gmail.com <giovanni.peccati(a)gmail.com> Cc: Kurusch Ebrahimi-Fard <kurusch.ebrahimi-fard(a)ntnu.no>, Joscha Diehl < joscha.diehl(a)gmail.com> Dear Giovanni, I hope all is well! A few months ago we advertised an open call for applications for a *PhD fellowship on time series analysis* here at NTNU in Trondheim. A joint project with Joscha DIEHL (cc) from Greifswald. Unfortunately, our top candidate eventually accepted another offer. Therefore, we've decided to re-open the position: https://www.jobbnorge.no/en/available-jobs/job/177938/phd-fellowship-in-mat… Joscha and I would appreciate it if you could bring this announcement to the attention of your students. Kind regards, Kurusch =========================================== Kurusch Ebrahimi-Fard Norwegian University of Science and Technology NTNU Department of Mathematical Sciences NO-7491 Trondheim, Norway Tel.: +47 735-935-27 (office) www: https://folk.ntnu.no/kurusche/ email: kurusch.ebrahimi-fard(a)ntnu.no <kurusch.ebrahimi-fard(a)ntnu.no> -- Prof. Giovanni Peccati ------------------------------------------ UR en Mathématiques Faculté des Sciences, de la Technologie et de la Communication ------------------------------------------ Université du Luxembourg ----------------------------------------- homepage: http://sites.google.com/site/giovannipeccati/Home E-mail: giovanni.peccati(a)gmail.com

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