Il Dipartimento di Scienze per l'Economia e l'Impresa (DISEI),
Università degli Studi di Firenze, ha bandito un posto di Ricercatore a
Tempo Determinato di tipo A nel SSD SECS-S/06 (Metodi Matematici
dell’Economia e delle Scienze Attuariali e Finanziarie).
Il bando si trova al link:
http://www.unifi.it/cmpro-v-p-11205.html
Le domande di partecipazione vanno inviate esclusivamente con la
modalità telematica descritta nel bando.
Il termine ultimo per l'invio è il 10 agosto 2017.
Cordiali saluti
Giacomo Scandolo
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>
===========================================================
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)
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
---- 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
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
--------------------------
-----------------------------------------------------------------------------
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/
-----------------------------------------------------------------------------
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/
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