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Data Science and Statistical Learning Post Doc positions - Human Technopole
by Piercesare Secchi 14 Jul '17

14 Jul '17

The Center for Analysis, Decision and Society of the Human Technopole, based in Milano, Italy (https://htechnopole.it/en/) is opening a few positions for post-doc in data science and statistical learning. Details can be found here: https://htechnopole.it/en/openings ------------------------------------------------ Piercesare Secchi MOX - Dipartimento di Matematica Politecnico di Milano Via Bonardi 9 20133 Milano - Italy tel. +39 02 2399 4592 fax. +39 02 2399 4568 e.mail: piercesare.secchi(a)polimi.it<mailto:piercesare.secchi@polimi.it>

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[Fwd: [gdre-conedp] Post-doctoral position at university Blaise Pascal (France)] (fwd)
by Tiziano Vargiolu 12 Jul '17

12 Jul '17

---------- Forwarded message ---------- Date: Wed, 12 Jul 2017 07:51:42 +0200 From: Fabio Ancona <ancona(a)math.unipd.it> To: users(a)math.unipd.it Subject: [Fwd: [gdre-conedp] Post-doctoral position at university Blaise Pascal (France)]

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2 postdocs in Geometry of Deep Learning and Reinforcement Learning at RIST
by Luigi Malagò 11 Jul '17

11 Jul '17

=========================================================== 2 postdoc positions in Machine Learning (1 year, renewable up to 3 years) Institution: RIST - Romanian Institute of Science and Technology, Cluj-Napoca Keywords: Deep Learning, Reinforcement Learning, Stochastic Optimization, Optimization over Manifolds, Information Geometry, Riemannian Geometry Application deadline: 30 July 2017 (applicants are encouraged to apply earlier) Salary: around 2190 euro net Official announcement: http://rist.ro/en/details/news/postdoc-positions-in-deep-learning-and-machi… =========================================================== Dear colleagues, the Romanian Institute of Science and Technology (RIST) has an opening for 2 postdoc positions, in the context of the DeepRiemann project “Riemannian Optimization Methods for Deep Learning”, funded by European structural funds through the Competitiveness Operational Program (POC 2014-2020). The appointments will be for 1 year, with possible extensions up to 3 years. The DeepRiemann project aims at the design and analysis of novel training algorithms for Neural Networks in Deep Learning, by applying notions of Riemannian optimization and differential geometry. The task of the training a Neural Network is studied by employing tools from Optimization over Manifolds and Information Geometry, by casting the learning process to an optimization problem defined over a statistical manifold, i.e., a set of probability distributions. The project is highly interdisciplinary, with competences spanning from Machine Learning to Optimization, Deep Learning, Statistics, and Differential Geometry. The objectives of the project are multiple and include both theoretical and applied research, together with industrial activities oriented to transfer knowledge, from the institute to a startup or spin-off of the research group. The positions will be part of the new Machine Learning and Optimization group http://luigimalago.it/group.html, which performs research at the intersection of Machine Learning, Stochastic Optimization, Deep Learning, and Optimization over Manifolds, from the unifying perspective of Information Geometry. The group is one of two newly-formed groups in Machine Learning at RIST, where about 20 new postdoctoral research associates and research software developers will be hired by 2018. The official job announcement can be seen here: http://rist.ro/en/details/news/postdoc-positions-in-deep-learning-and-machi… Informal inquiries can be sent to Dr. Luigi Malagò <malago(a)rist.ro>, principal investigator of the DeepRiemann project. Application deadline: 30 July 2017 (applicants are encouraged to apply earlier)

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Position of Lecturer/Senior Lecturer/Reader in Mathematics, University of Sussex
by Enrico Scalas 10 Jul '17

10 Jul '17

Car* Collegh*, al Dipartimento di Matematica dell'Universita' del Sussex stiamo reclutando in probabilita' e statistica. Vi prego di diffondere l'annuncio se conoscete persone interessate. I link sono: http://www.sussex.ac.uk/about/jobs/lecturer-senior-lecturer-in-mathematics-… http://www.jobs.ac.uk/job/BCQ418/lecturer-senior-lecturer-or-reader-in-math… Il profilo e': Applications are invited from all areas of mathematics strengthening and complementing the Department's research portfolio. Applications are particularly welcome from probability and statistics including, for example, mathematical statistics, rigorous data science, stochastic analysis, processes in random environments, and probabilistic modelling. Il bando contiene poi i link a "job description" e "person specification". Grazie per l'attenzione e cordiali saluti, Enrico Scalas Professor of Statistics and Probability Head of Department, Mathematics University of Sussex, UK

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deadline early-bird registration to the 11th ClaDAG Conference
by Francesca Greselin 10 Jul '17

10 Jul '17

---- We apologize for cross posting ---- Dear all, this is just a friendly reminder that *today* is the deadline for early-bird registration to the *11th Scientific Meeting of the CLAssification and Data Analysis Group of the Italian Statistical Society * http://www.cladag2017.unimib.it/ For the 2017 edition of the Conference, the University of Milano-Bicocca, Bocconi University, Politecnico di Milano, University of Milano and Università Cattolica del Sacro Cuore are cooperating and supporting the event, that will be hosted at University of Milano-Bicocca. *Papers* Around 150 papers have been submitted for the Conference, for which the usual blind referee process has been carried out by the Scientific Program Committee, chaired by Francesco Mola. *Keynote speakers:* - J. Sunil Rao, <http://www.biostat.med.miami.edu/people/primary-faculty/sunil-rao> Division of Biostatistics, Department of Public Health Sciences University of Miami, Florida, USA, - *Classified Mixed Model Prediction*- Antony Davison, <http://statwww.epfl.ch/davison/davison.html> Institute of Mathematics, Ecole Poytechnique Federale de Lausanne, Switzerland - *Statistical models for complex extremes*- Roberto Rocci <http://economia.uniroma2.it/faculty/145/rocci-roberto>, Dipartimento di Economia e Finanza, Università degli studi di Tor Vergata, Rome, Italy - *An URV approach to cluster ordinal data* *Young researchers data challenge*Young researchers are invited to a data challenge, at Politecnico di Milano the day before the Conference, say on September 12, and sponsored by Oracle and Data Reply. Amazing prices are announced for the winners! *Statistical Challenges in Big Data & Complex Systems* A satellite Conference will take place on Tuesday September 12, at the University of Milano. More information will be available soon at http://www.cladag2017.unimib.it/ *Social dinner*The social dinner will take place on September 14th, in the *San Nazaro Porch* of the ancient “*Cà Granda*” complex, entrusted to the Tuscan architect Antonio Averlino, known as Filarete. Founded on 12 April, 1456 by the Duke of Milan Francesco Sforza and his wife Bianca Maria, the gigantic complex was built to provide medical care for the city’s poorest people. *Conference Proceedings* The Conference Proceedings will be published in digital edition with ISBN and distributed for free to all the Cladag2017 Participants. *Post conference publications* After the Conference, the Program Committee encourages authors of the best papers to submit an extended version for possible publication in the *Springer Series:* "Studies in Classification, Data Analysis, and Knowledge Organization", or alternatively to submit to a *special issue* of the following journals: Statistical Analysis and Data Mining <http://www.statisticsviews.com/details/journal/2285261/Statistical-Analysis…>, published on behalf of the American Statistical Association <http://www.amstat.org/>, and Statistics and Applications <http://statisticaeapplicazioni.vitaepensiero.it/> Both journals will offer the possibility to publish a selection of the best 10-15 papers presented at CLADAG 2017. Topics covered in these papers should be in line with the aims and scope of the journal and will undergo a regular review process. We look forward to see you in Milan on 12-15 September for CLADAG 2017! The organizing Committee

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Fwd: Ph.D. position at Imperial college
by Marco Romito 07 Jul '17

07 Jul '17

Roth Studentship in Stochastic Analysis (for EU student) Imperial college, London The studentship is for a project in stochastic analysis, to work with Professor Xue-Mei Li starting 1st October 2017 or as soon as possible. The candidate should have a strong background in two of the following topics: stochastic analysis and probability; real analysis; pdes; and differential geometry. For some idea about stochastic analysis on manifolds, please see http://www.xuemei.org/Homepage/Research.html, See also http://www2.warwick.ac.uk/fac/sci/maths/people/staff/xue_mei_li/. The interested students should apply directly to Imperial college, mention this particulars. Applicants are directed to this page: http://www.imperial.ac.uk/mathematics/postgraduate/phd/how-to-apply <http://www.imperial.ac.uk/mathematics/postgraduate/phd/how-to-apply/> ------------------------------------------------------------------------------------------------------- Xue-Mei Li Professor Mathematics Institute The University of Warwick Coventry CV4 7AL U.K. Tel 44 (0) 2476 528 319 --------------------------

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Avviso di seminario: Cardinali@DISMEQ (Dipartimento di Statististica e Metodi Quantitativi, Milano-Bicocca)
by Fulvia Pennoni 06 Jul '17

06 Jul '17

----------------------------------------------------------------------------- Dipartimento di Statistica e Metodi Quantitativi Via Bicocca degli Arcimboldi, 8 - 20126 Milano ----------------------------------------------------------------------------- “COSTATIONARY INFERENCE FOR LOCALLY STATIONARY TIME SERIES” Alessandro Cardinali School of Computing and Mathematics, Plymouth University University of Plymouth Lunedì 10 Luglio ore 14.00 Ed. U7, 2° piano, aula 2061 __________________________________________________________ Abstract: In this presentation we illustrate a novel inferential approach to estimate time-varying parameters of locally stationary time series. This approach is based on costationary combinations, that is, time-varying deterministic linear combinations of locally stationary time series that are second-order stationary. We first review the theory of costationarity and formalize a Generalised Method of Moments estimator for the coefficient vectors. We then use this new framework to derive an estimator for the (time-varying) covariance of locally stationary time series and we show that the new covariance estimator is more efficient than classical estimators exclusively based on the evolutionary cross-periodogram. We confirm our theoretical findings through a simulation experiment. We then present a new analysis of financial log-returns showing that our new estimator is capable to highlight well known economic shocks. As a second example of our approach we finally discuss forecasting of locally stationary time series based on costationary combinations. __________________________________________________________ Cordiali saluti Fulvia Pennoni -- Fulvia Pennoni Department of Statistics and Quantitative Methods University of Milano-Bicocca http://www.statistica.unimib.it/utenti/pennoni/

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Avviso di seminario: Cardinali@DISMEQ (Dipartimento di Statististica e Metodi Quantitativi, Milano-Bicocca)
by Fulvia Pennoni 06 Jul '17

06 Jul '17

----------------------------------------------------------------------------- Dipartimento di Statistica e Metodi Quantitativi Via Bicocca degli Arcimboldi, 8 - 20126 Milano ----------------------------------------------------------------------------- “COSTATIONARY INFERENCE FOR LOCALLY STATIONARY TIME SERIES” Alessandro Cardinali School of Computing and Mathematics, Plymouth University University of Plymouth Lunedì 10 Luglio ore 14.00 Ed. U7, 2° piano, aula 2061 __________________________________________________________ Abstract: In this presentation we illustrate a novel inferential approach to estimate time-varying parameters of locally stationary time series. This approach is based on costationary combinations, that is, time-varying deterministic linear combinations of locally stationary time series that are second-order stationary. We first review the theory of costationarity and formalize a Generalised Method of Moments estimator for the coefficient vectors. We then use this new framework to derive an estimator for the (time-varying) covariance of locally stationary time series and we show that the new covariance estimator is more efficient than classical estimators exclusively based on the evolutionary cross-periodogram. We confirm our theoretical findings through a simulation experiment. We then present a new analysis of financial log-returns showing that our new estimator is capable to highlight well known economic shocks. As a second example of our approach we finally discuss forecasting of locally stationary time series based on costationary combinations. __________________________________________________________ Cordiali saluti Fulvia Pennoni -- Fulvia Pennoni Department of Statistics and Quantitative Methods University of Milano-Bicocca http://www.statistica.unimib.it/utenti/pennoni/

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3-year Ph.D. position at Bielefeld University
by Giorgio Ferrari 06 Jul '17

06 Jul '17

Dear Colleagues, this is to announce a *3-year Ph.D. position at Bielefeld University* starting on *October 1, 2017*. The successful candidate will participate in the Collaborative Research Centre (CRC) 1283 "Taming uncertainty and profiting from randomness and low regularity in analysis, stochastics and their applications" https://www.sfb1283.uni-bielefeld.de/Pages/home within the project C4 “Stochastic games of singular control and games of stopping”. More information on the post and on the application procedures can be found here http://www.uni-bielefeld.de/Universitaet/Aktuelles/Stellenausschreibungen/A… *DEADLINE FOR APPLICATION:* August 2, 2017 All the best, Giorgio Ferrari

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Postgraduate programmes at the University of Kent
by Fabrizio Leisen 03 Jul '17

03 Jul '17

Con preghiera di diffusione, Cordiali saluti, Fabrizio Leisen A postgraduate degree in Statistics from the University of Kent can open many doors both in industry and research. The taught programmes in Statistics and Statistics with Finance provide exemption from the professional examinations of the Royal Statistical Society and qualification for Graduate Statistician status. The School of Mathematics, Statistics and Actuarial Science has a strong reputation for world-class research and a well-established system of support and training, with a high level of contact between staff and research students. Postgraduate students develop analytical, communication and research skills. Developing computational skills and applying them to mathematical problems forms a significant part of the postgraduate training in the School. The School encourages all postgraduate statistics students to take part in statistics seminars and to help in tutorial classes. The Statistics Group is forward-thinking, with varied research, and received high rankings in the Research Excellence Framework (REF) 2014 for research power and quality. For more information about postgraduate statistics programmes at Kent visit www.kent.ac.uk/smsas or contact the Admissions Officer: E: smsaspgadmissions(a)kent.ac.uk T: +44 (0) 1227 816050 -- Fabrizio Leisen Reader in Statistics School of Mathematics, Statistics and Actuarial Sciences Sibson Building, University of Kent, Canterbury, CT2 7FS http://www.kent.ac.uk/smsas/statistics/our-people/profiles/leisen_fabrizio.… http://sites.google.com/site/fabrizioleisen/

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