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    The 18th International Conference, Graduate School of Mathematics, Nagoya University
    Information Geometry and Affine Differential Geometry III

    OFFICIAL WEBPAGE

    Period
    March 27–29, 2019

    Place
    Rm.~509, Mathematics Bldg., Nagoya University

    Speakers

    • Shun-ichi Amari (Riken),
    • Frédéric Barbaresco (Thales Land & Air Systems),
    • Michel Nguiffo Boyom (Université de Montpellier),
    • Shinto Eguchi (Institute of Statistical Mathematics),
    • Hitoshi, Furuhata (Hokkaido University),
    • Hiroto Inoue (Kyushu University),
    • Hideyuki Ishi (Nagoya University),
    • Amor Keziou (Université de Reims Champagne-Ardenne),
    • Yongdo Lim (Sungkyunkwan University),
    • Hiroshi Matsuzoe (Nagoya Institute of Technology),
    • Atsumi Ohara (Fukui University),
    • Philippe Regnault (Université de Reims Champagne-Ardenne),
    • Tatsuo Suzuki (Shibaura Institute of Technology),
    • Jun Zhang (University of Michigan)

    Organizing Committee

    • Hideyuki Ishi (Nagoya University),
    • Hiroshi Matsuzoe (Nagoya Institute of Technology),
    • Atsumi Ohara (Fukui University),
    • Jun Zhang (University of Michigan)

    Contact to
    Hideyuki Ishi (hideyuki (at) math.nagoya-u.ac.jp)

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    APPLICATION WEBSITE

    What you will do

    As part of the Research team, you will embrace theoretical mathematics, computer science and financial knowledge. Fully involved on advanced topics, you will work closely with researchers, deep learners, and science addicts. Being part of our international team, you will experience how being smartly wrong often brings to a better solution.

    Main missions:

    improve the learning capabilities of the automated trading systems
    design solutions to challenge large datasets with a data driven approach
    contribute to the research infrastructure aiming at identifying financial biais
    challenge researchers and common knowledge
    suggest and engage in team collaborations to meet research goals
    report and present research findings and developments

    Skills we are looking for

    PhD or MS in Data Science, Science Technology or Mathematics.

    You have:

    3-5 years of experience in Machine Learning
    a powerful intellectual curiosity with a strong academic knowledge in probability, statistics and machine learning models
    experience with Python3 and libraries such as Numpy, Pandas, Plotly
    experience with Linux and Git environments
    strong knowledge of object-oriented programming and algorithms
    a “can-do” attitude and a problem-solving mindset
    eager to learn and to challenge complex machine learning problems
    an ability to operate in an agile and fast-paced environment

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    APPLICATION WEBSITE
    Job Overview

    Advanced Analytics - Sr. Data Scientist will execute advanced computational approaches to aid in evidence-based pharmaceutical product development. He/She will leverage high-dimensional population health data to support R&D, Medical, HEVA, commercial product development, access and business strategy. The Advanced Analytics role will generate analytics required by healthcare decision makers to support patient access and use of Sanofi medicines and he/she will contribute to the insights required by Sanofi internal teams to develop and commercialize the most impactful medicines.

    Job Responsibilities

    Get to apply a broad array of capabilities spanning machine learning, statistics, text-mining/NLP, and modeling to extract insights to structured and unstructured healthcare data sources, pre-clinical, clinical trial and complementary real world information streams.
    Work on a variety of team-based projects providing expertise in analytical and computational approaches.
    Have the opportunity to identify novel solutions to internal analytics & data challenges including the piloting and/or evaluation of tools for analytics, reporting and data visualization.
    Develop additional skills through training courses, mentoring, and interactions daily with team members and Sanofi stakeholders.
    Provide expertise and execute advanced analytics for solving problems across R&D, Medical Affairs, HEVA and Market Access Strategies and Plans.
    Design and implement data models, perform statistical analysis and create predictive analysis models
    Translate and appropriately champion advanced analytics results and capabilities to non-technical audiences.
    Work with internal and external data scientists to scope and execute Advance Analytics projects.

    Essential Skills & Experience

    PhD or ScD in quantitative field such as Health Services research, Medical Economics, Medical Informatics, Biostatistics, or Computer Science, computer engineering or related field with a minimum of 3 years of industry or academic experience
    Relevant Masters Degree, with 6 or more years of related industry experience
    Proficiency in at least two or more technical or analytical languages (R, Python, etc..) and a willingness to embrace new coding approaches.
    Experience with advanced ML techniques (neural networks/deep learning, reinforcement learning, SVM, PCA, etc.).
    Demonstrated ability to interact with a variety of large-scale data structures e.g. HDFS, SQL, noSQL
    Experience working across multiple environments (e.g. AWS, GCP, linux) for optimizing compute and big data handling requirements.
    Experience with any of the following biomedical data types/population health data/real world data/novel data streams.
    Strong oral and written communication skills
    A demonstrated ability to work and collaborate in a team environment

    Desirable Skills & Experience

    Ability to prototype analyses and algorithms in high-level languages embracing reproducible and collaborative technology platforms (e.g. github, containers, jupyter notebooks)
    Exposure to NLP technologies and analyses
    Knowledge of some datavis technologies (ggplot2, shiny, plotly, d3, Tableau or Spotfire)

    Sanofi is committed to welcoming and integrating people with disabilities

    At Sanofi diversity and inclusion is foundational to how we operate and embedded in our Core Values. We recognize to truly tap into the richness diversity brings we must lead with inclusion and have a workplace where those differences can thrive and be leveraged to empower the lives of our colleagues, patients and customers. We respect and celebrate the diversity of our people, their backgrounds and experiences and provide equal opportunity for all.

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    APPLICATION WEBSITE

    THE ROLE

    You will join the Data Science team. It's a cross-functional team using data to support strategic decision-making and build better experiences for our passengers and drivers alike.

    As a Data Scientist focused on Algorithms, you will apply machine learning and optimisation techniques on our rich datasets to solve some of the most interesting mobility challenges, such as dynamic pricing, intelligent allocation and much more!

    WHAT YOU'LL DO

    • Work with Product to identify and prioritise algorithmic needs

    • Team up with Engineering to incorporate machine learning and optimisation algorithms in our product

    • Code simulation modules to replicate driver and passenger behaviours and suggest pricing or dispatch improvements

    • Uncover hidden opportunities for growth and efficiency for Heetch

    • Conduct and present quantitative analysis that results in actionable recommendations

    WHO WE ARE LOOKING FOR

    • You have a degree in Computer Science, Engineering, Economics, Physics, Statistics or another quantitative field (MS and above preferred)

    • You have 2+ years of industry experience in algorithm design and development

    • You are comfortable manipulating large datasets (using SQL, Python, R etc)

    • You can build and fit statistical, machine learning, or optimisation models

    • You can collaborate with Engineers to turn prototypes into scaled-up products

    • You can communicate effectively with colleagues from various backgrounds and technical levels

    • You are fluent in English

    BONUS POINTS IF YOU:

    • Have prior exposure to startup environments

    • Have experience with cloud computing and big data frameworks (incl. geospatial data)

    • Have experience leading machine learning projects and/or building data products end-to-end under limited supervision

    • Are able to model and run simulated and live traffic experiments

    • Are an explorer and enjoy going out!

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    UMAP - Leland McInnes, John Healy, James Melville

    GITHUB - OFFICIAL WEBSITE

    Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction. The algorithm is founded on three assumptions about the data

    • The data is uniformly distributed on a Riemannian manifold;
    • The Riemannian metric is locally constant (or can be approximated as such);
    • The manifold is locally connected.

    From these assumptions it is possible to model the manifold with a fuzzy topological structure. The embedding is found by searching for a low dimensional projection of the data that has the closest possible equivalent fuzzy topological structure.

    The details for the underlying mathematics can be found in our paper on ArXiv:

    • McInnes, L, Healy, J, UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, ArXiv e-prints 1802.03426, 2018

    The important thing is that you don't need to worry about that -- you can use UMAP right now for dimension reduction and visualisation as easily as a drop in replacement for scikit-learn's t-SNE.

    Documentation is available via ReadTheDocs.
    Installation, licence, how to use information is avalaible on
    GITHUB - OFFICIAL WEBSITE](https://github.com/lmcinnes/umap)

    Benefits of UMAP

    UMAP has a few signficant wins in its current incarnation.

    • First of all UMAP is fast. It can handle large datasets and high dimensional data without too much difficulty, scaling beyond what most t-SNE packages can manage.
    • Second, UMAP scales well in embedding dimension -- it isn't just for visualisation! You can use UMAP as a general purpose dimension reduction technique as a preliminary step to other machine learning tasks. With a little care (documentation on how to be careful is coming) it partners well with the hdbscan clustering library.
    • Third, UMAP often performs better at preserving aspects of global structure of the data than t-SNE. This means that it can often provide a better "big picture" view of your data as well as preserving local neighbor relations.
    • Fourth, UMAP supports a wide variety of distance functions, including non-metric distance functions such as cosine distance and correlation distance. You can finally embed word vectors properly using cosine distance!
    • Fifth, UMAP supports adding new points to an existing embedding via the standard sklearn transform method. This means that UMAP can be used as a preprocessing transformer in sklearn pipelines.
    • Sixth, UMAP supports supervised and semi-supervised dimension reduction. This means that if you have label information that you wish to use as extra information for dimension reduction (even if it is just partial labelling) you can do that -- as simply as providing it as the y parameter in the fit method.
    • Finally UMAP has solid theoretical foundations in manifold learning (see our paper on ArXiv). This both justifies the approach and allows for further extensions that will soon be added to the library (embedding dataframes etc.).

    Performance and Examples

    UMAP is very efficient at embedding large high dimensional datasets. In particular it scales well with both input dimension and embedding dimension. Thus, for a problem such as the 784-dimensional MNIST digits dataset with 70000 data samples, UMAP can complete the embedding in around 2.5 minutes (as compared with around 45 minutes for most t-SNE implementations). Despite this runtime efficiency UMAP still produces high quality embeddings.

    The obligatory MNIST digits dataset, embedded in 2 minutes and 22 seconds using a 3.1 GHz Intel Core i7 processor (n_neighbors=10, min_dist=0 .001):

    UMAP embedding of MNIST digits

    umap_example_mnist1.png

    The MNIST digits dataset is fairly straightforward however. A better test is the more recent "Fashion MNIST" dataset of images of fashion items (again 70000 data sample in 784 dimensions). UMAP produced this embedding in 2 minutes exactly (n_neighbors=5, min_dist=0.1):

    UMAP embedding of "Fashion MNIST"
    umap_example_fashion_mnist1.png

    The UCI shuttle dataset (43500 sample in 8 dimensions) embeds well under correlation distance in 2 minutes and 39 seconds (note the longer time required for correlation distance computations):

    UMAP embedding the UCI Shuttle dataset
    umap_example_shuttle.png

    posted in GSI FORGE read more
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    Shape Analysis, Stochastic Mechanics and Optimal Transport
    OFFICIAL WEBSITE

    • Boris Khesin, University of Toronto: Beyond Arnold’s geodesic framework of an ideal hydrodynamics
      We discuss a ramification of Arnold’s group-theoretic approach to ideal hydrodynamics as the geodesic flow for a right-invariant metric on the group of volume-preserving diffeomorphisms. We show such problems of mathematical physics as the motion of vortex sheets or fluids with moving boundary, have Lie groupoid, rather than Lie group, symmetries, and describe the corresponding geometry and equations. (This is a joint work with Anton Izosimov.)
      Watch video | Download video
    • Gerard Misiolek, University of Notre Dame: The L2 exponential map in 2D and 3D hydrodynamics
      In the 1960's V. Arnold showed how solutions of the incompressible Euler equations can be viewed as geodesics on the group of diffeomorphisms of the fluid domain equipped with a metric given by fluid's kinetic energy. The study of the exponential map of this metric is of particular interest and I will describe recent results concerning its properties as well as some necessary background.
      Watch video | Download video
    • Klas Modin, Chalmers University of Technology / University of Gothenburg: Semi-invariant metrics on diffeos
      We investigate a generalization of cubic splines to Riemannian manifolds. Spline curves are defined as minimizers of the spline energy---a combination of the Riemannian path energy and the time integral of the squared covariant derivative of the path velocity---under suitable interpolation conditions. A variational time discretization for the spline energy leads to a constrained optimization problem over discrete paths on the manifold. Existence of continuous and discrete spline curves is established using the direct method in the calculus of variations. Furthermore, the convergence of discrete spline paths to a continuous spline curve follows from the Γ-convergence of the discrete to the continuous spline energy. Finally, selected example settings are discussed, including splines on embedded finite-dimensional manifolds, on a high-dimensional manifold of discrete shells with applications in surface processing, and on the infinite-dimensional shape manifold of viscous rods.
      Watch video | Download video
    • Ana Cruzeiro, University of Lisbon : On some relations between Optimal Transport and Stochastic Geometric Mechanics
      We formulate the so-called Schrodinger problem in Optimal Transport on lie group and derive the corresponding Euler-Poincaré equations.
      Watch video | Download video | PDF presentation
    • Christian Léonard, Universite Paris Nanterre: Some ideas and results about gradient flows and large deviations
      In several situations, the empirical measure of a large number of random particles evolving in a heat bath is an approximation of the solution of a dissipative PDE. The evaluation of the probabilities of large deviations of this empirical measure suggests a way of defining a natural ``large deviation cost'' for these fluctuations, very much in the spirit of optimal transport. Some standard Wasserstein gradient flow evolutions are revisited in this perspective, both in terms of heuristic results and a few rigorous ones. This talk gathers several joint works with Julio Backhoff, Giovanni Conforti, Ivan Gentil, Luigia Ripani and Johannes Zimmer.
      Watch video | Download video | PDF presentation
    • Marc Arnaudon, Université de Bordeaux: A duality formula and a particle Gibbs sampler for continuous time Feynman-Kac measures on path spaces
      "Continuous time Feynman-Kac measures on path spaces are central in applied probability, partial differential equation theory, as well as in quantum physics. I will present a new duality formula between normalized Feynman-Kac distribution and their mean field particle interpretations. Among others, this formula will allow to design a reversible particle Gibbs-Glauber sampler for continuous time Feynman-Kac integration on path spaces. This result extends the particle Gibbs samplers introduced by Andrieu-Doucet-Holenstein in the context of discrete generation models to continuous time Feynman-Kac models and their interacting jump particle interpretations. I will also provide new propagation of chaos estimates for continuous time genealogical tree based particle models with respect to the time horizon and the size of the systems. These results allow to obtain sharp quantitative estimates of the convergence rate to equilibrium of particle Gibbs-Glauber samplers. "
      Watch video | Download video | PDF presentation
    • Alexis Arnaudon, Imperial College London : Geometric modelling of uncertainties
      In mechanics, and in particular in shape analysis, taking into account the underlying geometric properties of a problem to model it is often crucial to understand and solve it. This approach has mostly been applied for isolated systems, or for systems interacting with a well-defined, deterministic environment. In this talk, I want to discuss how to go beyond this deterministic description of isolated systems to include random interactions with an environment, while retaining as much as possible the geometric properties of the isolated systems. I will discuss examples from geometric mechanics to shape analysis, ranging from interacting rigid bodies with a heath bath to uncertainties quantification in computational anatomy.
      Watch video | Download video
    • Bernhard Schmitzer, University of Münster: Semi-discrete unbalanced optimal transport and quantization
      "Semi-discrete optimal transport between a discrete source and a continuous target has intriguing geometric properties and applications in modelling and numerical methods. Unbalanced transport, which allows the comparison of measures with unequal mass, has recently been studied in great detail by various authors. In this talk we consider the combination of both concepts. The tessellation structure of semi-discrete transport survives and there is an interplay between the length scales of the discrete source and unbalanced transport which leads to qualitatively new regimes in the crystallization limit."
      Watch video | Download video | PDF presentation
    • Carola-Bibiane Schönlieb, University of Cambridge : Wasserstein for learning image regularisers
      In this talk we will discuss the use of a Wasserstein loss function for learning regularisers in an adversarial manner. This talk is based on joint work with Sebastian Lunz and Ozan Öktem, see https://arxiv.org/abs/1805.11572
      Watch video | Download video | PDF presentation
    • Tryphon Georgiou, University of California, Irvine : Interpolation of Gaussian mixture models and other directions in Optimal Mass Transport
      Watch video | Download video
    • Laurent Younes, John Hopkins University : Normal coordinates and equivolumic layers estimation in the cortex (tentative)
      Watch video | Download video
    • Barbara Gris, Université Pierre-et-Marie-Curie: Analyze shape variability via deformations
      I will present how shape registration via constrained deformations can help understanding the variability within a population of shapes.
      Watch video | Download video
    • Dongyang Kuang, University of Ottawa : Convnets, a different view of approximating diffeomorphisms in medical image registration
      As with the heat of artificial intelligence, there are more and more researches starting to investigate the possible geometric transformations using data-driven methods such as convolutional neural networks. In this talk, I will start by introducing some existing work that learn 2D linear transformations in an unsupervised way. This then will be followed by an overview of some recent works focusing on nonlinear transformations in 3D volumetric data. Finally, I will present results from the joint work with my supervisor using our network architecture called FAIM.
      Watch video | Download video
    • Stephen Preston, Brooklyn College : Solar models for Euler-Arnold equations
      Many one-dimensional Euler-Arnold equations can be recast in the form of a central-force problem Γtt(t,x)=−F(t,x)Γ(t,x), where Γ is a vector in ℝ2 and F is a nonlocal function possibly depending on Γ and Γt. Angular momentum of this system is precisely the conserved momentum for the Euler-Arnold equation. In particular this picture works for the Camassa-Holm equation, the Hunter-Saxton equation, and the Okamoto-Sakajo-Wunsch family of equations. In the solar model, breakdown comes from a particle hitting the origin in finite time, which is only possible with zero angular momentum. Results due to McKean (for Camassa-Holm), Lenells (for Hunter-Saxton), and Bauer-Kolev-Preston/Washabaugh (for the Wunsch equation) show that breakdown of smooth solutions occurs exactly when momentum changes from positive to negative. I will discuss some conjectures and numerical evidence for the generalization of this picture to other equations such as the μ-Camassa-Holm equation or the DeGregorio equation.
      Watch video | Download video
    • Cy Maor, University of Toronto : Vanishing geodesic distance for right-invariant Sobolev metrics on diffeomorphism groups
      Since the seminal work of Arnold on the Euler equations, many important PDEs were shown to be geodesic equations of diffeomorphism groups of manifolds, with respect to various Sobolev norms. But what about the geodesic distance induced by these norms? Is it positive between different diffeomorphisms, or not? In this talk I will show that the geodesic distance on the diffeomorphism group of an n-dimensional manifold, induced by the Ws,p norm, does not vanish if and only if s≥1 or sp>n. The first condition detects changes of volume, while the second one detects transport of arbitrary small sets. I will focus on the case where both conditions fail, and how this enables the construction of arbitrary short paths between diffeomorphisms. Based on a joint work with Robert Jerrard, following works of Michor-Mumford, Bauer-Bruveris-Harms-Michor and Bauer-Harms-Preston.
      Watch video | Download video
    • Philipp Harms, University of Freiburg : Smooth perturbations of the functional calculus and applications to Riemannian geometry on spaces of metrics.
      We show that the functional calculus, which maps operators A to functionals f(A), is holomorphic for a certain class of operators A and holomorphic functions f. Using this result we are able to prove that fractional Laplacians depend real analytically on the underlying Riemannian metric in suitable Sobolev topologies. As an application we obtain local well-posedness of the geodesic equation for fractional Sobolev metrics on the space of all Riemannian metrics. (Joint work with Martins Bruveris, Martin Bauer, and Peter W. Michor).
      Watch video | Download video
    • Eric Klassen, Florida State University : Comparing Shapes of Curves, Surfaces, and Higher Dimensional Immersions in Euclidean Space.
      Comparing shapes and treating them as data for statistical analyses has many applications in biology and elsewhere. Certain elastic metrics on spaces of immersions have proved very effective for comparing curves and surfaces. The elastic metrics which have proved most useful for computation have been first order metrics, i.e., they compare tangent vectors on the shapes rather than points on the shapes. In this talk I will present a unifying view of these metrics, shedding new light on old methods and, I hope, suggesting new methods for analyzing surfaces and higher dimensional shapes.
      Watch video | Download video
    • Facundo Memoli, The Ohio State University : Metrics on the collection of dynamic shapes.
      When studying flocking/swarming behaviors in animals one is interested in quantifying and comparing the dynamics of the clustering induced by the coalescence and disbanding of groups of animals. In a similar vein, when attempting to classify motion capture data according to action one is confronted with having to match/compare shapes that evolve with time. Motivated by these applications, we study the question of suitably metrizing the collection of all dynamic metric spaces (DMSs). We construct a suitable metric on this collection and prove the stability of several natural invariants of DMSs under this metric. In particular, we prove that certain zigzag persistent homology invariants related to dynamic clustering are stable w.r.t. this distance. These lower bounds permit the efficient classification of dynamic shape data in applications. We will show computational experiments on dynamic data generated via distributed behavioral models. This is joint work with Woojin Kim and Zane Smith https://research.math.osu.edu/networks/formigrams/
      Watch video | Download video
    • Tom Needham, Ohio State University : Gromov-Monge Quasimetrics and Distance Distributions.
      In applications in computer graphics and computational anatomy, one seeks a measure-preserving map from one shape to another which preserves geometry as much as possible. Inspired by this, we consider a notion of distance between arbitrary compact metric measure spaces by blending the Monge formulation of optimal transport with the Gromov-Hausdorff construction. We show that the resulting distance is an extended quasi-metric on the space of compact mm-spaces. This distance has convenient lower bounds defined in terms of distance distributions; these are functions associated to mm-spaces which have been used frequently as summaries in data and shape analysis applications. We provide rigorous results on the effectiveness of these lower bounds when restricted to simple classes of mm-spaces such as metric graphs or plane curves.This is joint work with Facundo Mémoli.
      Watch video | Download video
    • Jean-David Benamou, INRIA Rocquencourt : Dynamic formulations of optimal transportation and variational relaxation of Euler equations.
      We will briefly recall the classical Optimal Transportation Framework and its Dynamic relaxations. We will show the link between these Dynamic formulation and the so-called MultiMarginal extension of Optimal Transportation. We will then describe the so-called Iterative Proportional Fitting Procedure (aka Sinkhorn method) which can be efficiently applied to the multi-marginal OT setting. Finally we will show how this can be used to compute generalized Euler geodesics due to Brenier. This problem can be considered as the oldest instance of Multi-Marginal Optimal Transportation problem. Joint work with Guillaume Carlier (Ceremade, Universite Paris Dauphine, France) and Luca Nenna (U. Paris Sud, France).
      Watch video | Download video
    • Tudor Ratiu, Shanghai Jiao Tong University: Group valued momentum maps
      Watch video | Download video
    • Andrea Natale, Inria : Generalized H(div) geodesics and solutions of the Camassa-Holm equation
      Watch video | Download video
    • Jean Feydy, Ecole Normale Supérieure : Robust shape matching with optimal transport
      Watch video | Download video | PDF presentation
    • Alice Le Brigant*, ENAC - Ecole Nationale de l'Aviation Civile : Quantization on a Riemannian manifold with application to air traffic control
      Watch video | Download video

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    Shape Analysis, Stochastic Mechanics and Optimal Transport
    OFFICIAL WEBSITE

    Organizers

    Description

    The Banff International Research Station will host the "Shape Analysis, Stochastic Geometric Mechanics and Applied Optimal Transport" workshop from December 9th to December 14th, 2018.

    The comparison and analysis of shapes, whether of organs, cells or engineering structures such as airfoils, pose important mathematical and statistical challenges. Shape analysis has recently seen a tremendous development in both theory and practice, driven by a wide range of applications from biological imaging to fluid dynamics. For example, organ shapes observed in medical images can now be used for diagnostic and prognostic purposes, and optimization of shapes has become an important tool in engineering.

    On the theoretical side, recent developments have highlighted the strong connections between shape analysis and the related fields of optimal transport and stochastic geometric mechanics, both very active fields in their own right. The workshop aims to bring together researchers in these three fields, to share methodological developments and open problems, and generally to link and accelerate research in all three fields.

    The Banff International Research Station for Mathematical Innovation and Discovery (BIRS) is a collaborative Canada-US-Mexico venture that provides an environment for creative interaction as well as the exchange of ideas, knowledge, and methods within the Mathematical Sciences, with related disciplines and with industry. The research station is located at The Banff Centre in Alberta and is supported by Canada's Natural Science and Engineering Research Council (NSERC), the U.S. National Science Foundation (NSF), Alberta's Advanced Education and Technology, and Mexico's Consejo Nacional de Ciencia y Tecnología (CONACYT).

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    Journée ISS France
    OFFICIAL WEBSITE
    iss.png

    Dates
    La 42ème édition de la journée ISS France aura lieu le Jeudi 07 Février 2019

    Lieu
    Ecole des Mines ParisTech 60, boulevard Saint-Michel 75272 Paris cedex 06

    Organisateurs

    • Corinne Lagorre, Université Paris Est Créteil, LISSI, 61 avenue du Gal de Gaulle, 94000 Créteil
    • Bruno Figliuzzi, Centre de Morphologie Mathématique, 35 rue Saint Honoré, 77305 Fontainebleau

    Inscriptions
    La participation est gratuite mais l'inscription est obligatoire. Vous pouvez vous inscrire ou proposer une communication par par mail à l'adresse: bruno.figliuzzi-at-mines-paristech.fr

    Description

    Les journées d’étude de l’ISS France (International Society for Stereology) rassemblent chaque année des acteurs de l’analyse des images numériques, de la stéréologie et de leurs applications et connexions. La volonté a été affirmée depuis de nombreuses années de faire des journées d’étude de l’ISS France un lieu de rencontre, d’échange et d’expérimentation réellement pluridisciplinaire, qui puise ses forces dans l’ensemble du patrimoine intellectuel actuel. Le point d’ancrage reste délibérément l’image, et les techniques, sciences, applications et arts qui s’y intéressent.

    Vous trouverez sur cette page les programmes des deux dernières journées d’étude ; vous pourrez en particulier y découvrir les thématiques classiquement abordées:

    • La Session Méthodes est principalement axée sur les techniques issues de la morphologie mathématique,
    • Les sessions Applications parcourent les principaux travaux réalisés dans les domaines des Biosciences, des Sciences des Matériaux ou de la Géographie Mathématique, notamment.

    posted in ISS 2019 read more
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    Applications are invited for a 12-month-postdoctoral fellowship in discrete probability at Université Jean Monnet, Institut Camille Jordan. The position is funded by IDEX Lyon IMPULSION (you may also click on this link for more details: https://math.unice.fr/~dmitsche/Postdoc.pdf)

    Research areas: random graphs, random geometric (hyperbolic) graphs, random walks, discrete probability in general.

    Practical informations:

    • The position is for one year (12 months) with a salary of approx. 27K Euro (in the case of less than 3 years experience after the PhD) and approx. 34K Euro (between 3 and 6 years experience after the PhD).
    • Financial support to attend workshops / invite collaborators will be granted.
    • No teaching
    • A lot of activity around discrete probability going on
    • The starting date is flexible, between February and September 2019. The candidate should indicate the preferred starting date in the application letter.

    Application and deadline:
    Applications including a CV, a list of publications and an approximately two-page description of research interests should be sent by email to Dieter Mitsche: dmitsche [at] gmail.com.
    Applicants should also arrange two recommendation letters (to be sent to the same address). Informal inquiries can be sent to the same address. Deadline for applications: January 15th, 2019, and then on a rolling basis

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    APPLICATION WEBPAGE

    Job description

    The University of Limerick (UL) with over 15,000 students and 1,400 staff is an energetic and enterprising institution with a proud record of innovation and excellence in education, research and scholarship. The dynamic, entrepreneurial and pioneering values which drive UL’s mission and strategy ensures that we capitalise on local, national and international engagement and connectivity. We are renowned for providing an outstanding student experience and conducting leading edge research. Our commitment is to make a difference by shaping the future through educating and empowering our students. UL is situated on a superb riverside campus of over 130 hectares with the River Shannon as a unifying focal point. Outstanding recreational, cultural and sporting facilities further enhance this exceptional learning and research environment.

    Applications are invited for the following position:

    Faculty of Science + Engineering

    Department of Mathematics & Statistics

    Lecturer and Lecturer below the bar in Statistics & Data Analytics

    Contract Type: Lecturer – Multiannual (Post 1)

    Contract Type: Lecturer below the bar (Post 2) - Tenure Track (five year fixed term).

    During the term of the contract the successful applicant will have the opportunity to apply for tenure in accordance with the University's Policy and Procedures for Granting Multi-annual Status to Tenure Track Academic Staff

    Salary Scale: Lecturer €52,187 - €83,038 p.a.

    Salary Scale: Lecturer below the bar €39,118 - €53,782 p.a.

    Further information for applicants and application material is available online.

    The closing date for receipt of applications is Monday, 3rd December 2018.

    Applications must be completed online before 12 noon, Irish Standard Time on the closing date.

    Please email erecruitment@ul.ie if you experience any difficulties

    Applications are welcome from suitably qualified candidates.

    The University of Limerick holds a Bronze Athena SWAN award in recognition of our commitment to advancing equality in higher education. The University is an equal opportunities employer and is committed to selection on merit welcoming applicants from all sections of the community. The University has a range of initiatives to support a family friendly working environment, including flexible working.

    “The University of Limerick has implemented a “Smoke and Vape Free Campus Policy”. Smoking and vaping in all forms is prohibited.”

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