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NADIGEST  May 2021, Week 5

NADIGEST May 2021, Week 5

Subject:

NA Digest, V. 21, # 20

From:

NA Digest Editor <[log in to unmask]>

Reply-To:

NA-Digest <[log in to unmask]>

Date:

Sun, 30 May 2021 09:35:55 -0600

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (550 lines)

Subject: NA Digest, V. 21, # 20

NA Digest  Sunday, May 30, 2021  Volume 21 : Issue 20

Today's Editor:

        Daniel M. Dunlavy
        Sandia National Labs
        [log in to unmask]

Today's Topics:

        New Book, Finite Elements
        Computational Machine Learning Course, USA, Aug-Nov, 2021
        Constructive Approximation and Applications, ONLINE, Sep 2021
        Wave Phenomena: Analysis and Numerics, Germany, Sep 2021
        Professor of Practice Position, Data Analytics/Modeling, Virginia Tech
        Postdoc Position, Biomathematics/Sci Comp, Univ of Milan
        Postdoc Position, Computational PDEs
        Postdoc Position, Coupled Data Assimilation, Univ of Maryland/ESSIC
        Postdoc Position, KAUST
        Postdoc Position, Optimization, HSU Hamburg, Germany
        Postdoc Position, Scientific ML, Sandia National Labs
        Postdoc Positions, NIST NRC
        PhD/Postdoc Positions, Data Assimilation, Germany
        PhD Position, Applied Mathematics, HSU/UniBw, Germany
        PhD Position, UQ for Neuroscience Applications
        Contents, MMA Journal, 26 (2)

Subscribe, unsubscribe, change address, or for na-digest archives:
        http://www.netlib.org/na-digest-html/faq.html

Submissions for NA Digest:
        http://icl.utk.edu/na-digest/

-------------------------------------------------------

From: Alexandre Ern [log in to unmask]
Date: May 25, 2021
Subject: New Book, Finite Elements

We are glad to announce the publication of the three volumes of our
monograph on finite elements:

Finite Elements, Texts in Applied Mathematics (TAM), Springer 2021
- I. Approximation and Interpolation (TAM volume 72)
- II. Galerkin Approximation, Elliptic and Mixed PDEs (TAM volume 73)
- III. First-Order and Time-Dependent PDEs (TAM volume 74)

https://link.springer.com/book/10.1007/978-3-030-56341-7
https://link.springer.com/book/10.1007/978-3-030-56923-5
https://link.springer.com/book/10.1007/978-3-030-57348-5

Each volume is a textbook that is suitable for graduate coursework,
academic research, and professional engineering. One salient feature
of the monograph is the organization of the material into relatively
short chapters (12 to 14 pages on average).  Each chapter can be
covered in one teaching unit and includes exercises.  The organization
into short chapters will also be useful to experts looking for
references to the literature on some specialized topics. Altogether,
the three volumes are organized into 83 chapters and contain about 500
exercises. A fourth volume with the solutions to all the exercises is
available at https://hal.archives-ouvertes.fr/hal-03226052

Alexandre Ern and Jean-Luc Guermond

-------------------------------------------------------

From: Raj Rao [log in to unmask]
Date: May 25, 2021
Subject: Computational Machine Learning Course, USA, Aug-Nov, 2021

Announcing enrollment for the Fall cohort of U. Michigan Online Course
on Computational Machine Learning for Scientists and Engineers.
Dates: August 11 - November 17, 2021
Enroll by: July 11, 2021
https://continuum.engin.umich.edu/programs/jumpstart-ml/

The Continuum Jumpstart Course Computational Machine Learning (ML) for
Scientists and Engineers is designed to equip you with the knowledge
you need to understand, train, and design machine learning algorithms,
particularly deep neural networks, and even deploy them on the cloud.
You'll learn by programming machine learning algorithms from scratch
in a hands-on manner using a one-of-a-kind cloud-based interactive
computational textbook that will guide you, and check your progress,
step-by-step. Using real-world datasets and datasets of your choosing,
you will understand, and we will discuss, via computational discovery
and critical reasoning, the strengths and limitations of the
algorithms and how they can or cannot be overcome. You will understand
how machine learning algorithms do what they claim to do so you can
reproduce these while being able to reason about and spot wild,
unsupported claims of their efficacy.  By the end of the course, you
will be ready to harness the power of machine learning in your daily
job and prototype, we hope, innovative new ML applications for your
company with datasets you alone have access to.

For testimonials from previous participants see
https://continuum.engin.umich.edu/programs/jumpstart-ml/testimonials-and-advice/

-------------------------------------------------------

From: Stefano De Marchi [log in to unmask]
Date: May 24, 2021
Subject: Constructive Approximation and Applications, ONLINE, Sep 2021

On behalf of the organizing committee of the *5th Dolomites Workshop
on Constructive Approximation and Applications* which will be ONLINE
from 6 to 10 September 2021, I inform that registration is open:

https://events.math.unipd.it/dwcaa21/

-------------------------------------------------------

From: Marlis Hochbruck [log in to unmask]
Date: May 25, 2021
Subject: Wave Phenomena: Analysis and Numerics, Germany, Sep 2021

The Collaborative Recearch Centre 1173 at Karlsruhe Institute of
Technology (https://www.waves.kit.edu/) invites applications for the
Summer School on "Wave Phenomena: Analysis and Numerics". The school
takes place from September 27 to 30, 2021.

It is directed to PostDocs, PhDs and advanced master
students. Lecturers will be Lukas Einkemmer (University of Innsbruck)
and Anna Geyer (TU Delft).

The event is planned on-site, but also as a world where "roomies and
zoomies" can co-exist. The deadline for registration is June 30th,
2021.  There is no registration fee.

Details and further information can be found here:
https://s.kit.edu/zw2m2xjk.

-------------------------------------------------------

From: Mark Embree [log in to unmask]
Date: May 28, 2021
Subject: Professor of Practice Position, Data Analytics/Modeling, Virginia Tech

Virginia Tech seeks applicants for a Professor of Practice position in
Computational Modeling and Data Analytics (CMDA), to support
project-based learning in CMDA's Capstone Project program.

Virginia Tech established the undergraduate CMDA major in 2015, and a
capstone project has been a fundamental requirement of the degree from
the start.  Teams comprising 3-4 students spend a semester tackling
nontrivial open-ended projects proposed by clients from industry or
academia.  The course instructors mentor these teams in problem
solving, project management, team dynamics, leadership, technical
communication, and career skills.

Applicants must have a strong background in computational science or
data analytics, with demonstrated leadership solving interdisciplinary
problems in a business, government, or research setting; the ability
to teach students with diverse interests, backgrounds, and abilities
as they tackle open-ended, ambiguous problems; and
commitment/sensitivity to address issues of diversity in the
university community. Applicants must have earned a master's degree or
doctorate in Computer Science, Mathematics, Statistics, or a related
field at the time of appointment.

For application details, see:
https://careers.pageuppeople.com/968/cw/en-us/job/515802

-------------------------------------------------------

From: Simone Scacchi [log in to unmask]
Date: May 29, 2021
Subject: Postdoc Position, Biomathematics/Sci Comp, Univ of Milan

The group of Numerical Analysis at the Department of Mathematics,
University of Milan, invites applications for a PostDoc position (24
months) in the area of biomathematics and scientific computing.  The
deadline for applications is June 30, 2021 at 11:59 AM (Rome time):
https://www.unimi.it/sites/default/files/2021-
05/bando%20assegni%20A%202021_signed.pdf

Advisors will be Paola Causin and Simone Scacchi.
Expected starting date will be in Fall 2021.

Objectives of the research activity will be among the following,
depending on the experience of the successful candidate:
Development of effective numerical methods and parallel solvers for
fluid-structure interaction, with applications to hemodynamics;
Development of machine learning techniques for the approximation of
partial differential equations, with applications to hemodynamics;
Development of effective numerical methods and parallel solvers for
the cardiac electro-mechanical activity.

Competences: Mathematical modeling; Numerical analysis and scientific
computing; Numerical methods for partial differential equations;
Machine learning; Programming in C++ and/or Python.

Paola Causin and Simone Scacchi

-------------------------------------------------------

From: Andrea Cangiani [log in to unmask]
Date: May 25, 2021
Subject: Postdoc Position, Computational PDEs

The mathLab group at the International School for Advanced Studies
(SISSA) in Trieste, Italy, invites applications for a research
fellowship position in Numerical Analysis of PDEs, under the
supervision of Andrea Cangiani starting anytime before March 2022. The
post holder is expected to contribute to the research of the mathLab
group on general adaptive methods for multiphysics problems such as
moving interface problems, phase transition, FSI. We seek a candidate
with strong background in computational PDEs - theory and
implementation. Prior experience in HPC and specifically modelling and
simulation of industrial and/or biomedical applications is desirable.

The duration is 1+1 years (one year plus a further year depending on
performance). Applications info, details and requirements are
available at the link:
https://www.sissa.it/bandi/selezione-pubblica-titoli-conferimento-di-n-1-assegno-di-
ricerca-fse-area-matematica-ref-prof
(Click file Announcement_FSE_Cangiani.pdf for English version.)

Deadline for Applications: 19/07/2021.

For enquiries/further info and details, email [log in to unmask]

-------------------------------------------------------

From: santha akella [log in to unmask]
Date: May 24, 2021
Subject: Postdoc Position, Coupled Data Assimilation, Univ of Maryland/ESSIC

UMD/ESSIC seeks to fill a postdoctoral research associate position to
study coupled data assimilation strategies within the NASA GEOS
coupled forecast system. This work will be performed at the Global
Modeling and Assimilation Office (GMAO), NASA, Goddard Space Flight
Center (GSFC) in Greenbelt, MD, USA.

Please see following for further details.:
http://essic.umd.edu/joom2/index.php/employment/3085-postdoctoral-associate-in-
coupled-data-assimilation

-------------------------------------------------------

From: David Ketcheson [log in to unmask]
Date: May 30, 2021
Subject: Postdoc Position, KAUST

A postdoctoral fellowship in applied mathematics is currently
available in the Numerical Mathematics Group, lead by Prof. David
Ketcheson, at King Abdullah University of Science and Technology
(KAUST).

Depending on the interests and expertise of the applicant, research in
this position may be purely theoretical, heavily computational, or
somewhere in between.  Current areas of research in the group include:
- Structure-preserving numerical time integrators
- Structure-preserving discretizations of hyperbolic PDEs
- Analysis and computation of novel water wave phenomena
- Optimal control in epidemiology

Examples of current and past research in the group can be found at
https://numerics.kaust.edu.sa/publications.html.  Postdoctoral fellows
in the group are expected to take an active role in determining the
direction of their own research and have the opportunity to work
closely with masters and doctoral students.

Candidates should have a PhD in applied mathematics or a
closely-related field and expertise in numerical analysis, PDEs,
and/or scientific computing.  The position includes a highly
competitive tax-free salary as well as free housing and health
insurance, along with generous funding for computing equipment and
conference travel.  The position is renewable for up to three
years. Please send a CV and research statement to
[log in to unmask]


-------------------------------------------------------

From: Kathrin Welker [log in to unmask]
Date: May 25, 2021
Subject: Postdoc Position, Optimization, HSU Hamburg, Germany

The Helmut-Schmidt-University / University of the Federal Armed Forces
in Hamburg (Germany) offers a position as a Postdoctoral Researcher
(f/m/d).  The position is a full-time position and complete funding
for 3 years is available.

The successful candidate should support and participate in current
research projects of the working group, in particular the analytical
and numerical investigation of shape optimization problems, the
development of optimization methods and the investigation of
optimization methods on shape spaces like manifolds or diffeological
spaces. For more information about research topics and projects of the
working group, please see https://www.hsu-hh.de/mathematik/forschung/

We support the active participation in national and international
scientific conferences and summer schools, possibility to participate
in acquiring third-party funds and the generation of scientific
publications in renowned international journals.  You will be equipped
with state-of-the art IT devices (laptop, tablet, desktop PC or
Mac). Access to a powerful high performance cluster on campus is
granted.  Requirements for employment are a PhD in mathematics, a
strong background in optimization or numerical mathematics,
fundamental knowledge in differential geometry and programming skills.

For the job announcement, please see
https://www.hsu-hh.de/karriere/wp-content/uploads/sites/658/2021/05/Kennziffer-BIW-
0921.pdf

The job announcement is formulated in German but of course
applications from international applicants are very welcome. For any
questions, please contact Prof. Dr. Kathrin Welker ([log in to unmask]).
Your application should include at least a cover letter, CV and
certificates of academic degrees. Moreover, letters of recommendation
are very welcome. Applications should be directed by e-mail to
[log in to unmask] by mentioning the reference number BIW-
0921 by June 17, 2021.


-------------------------------------------------------

From: Michael Parks [log in to unmask]
Date: May 26, 2021
Subject: Postdoc Position, Scientific ML, Sandia National Labs

Our team is seeking a postdoctoral appointee at the Computer Science
Research Institute with a strong background in the development of
numerical methods for solving problems in computational science,
machine learning (ML), and high-performance computing (HPC). The
successful candidate will contribute to an effort developing numerical
methods for training deep neural networks on leadership class HPC
platforms. This work is targeting applications in scientific machine
learning, where the appointee will work in a team of scientists to
apply their training methods and neural network technologies to
problems of broad interest to the science and engineering community,
including applications in climate science and plasma physics.

To Apply:
Go to http://www.sandia.gov/careers/students_postdocs/postdocs.html
Click 'View All Jobs'
Search for Job ID: 676654

Qualifications We Require: Possess, or are pursuing, a PhD in
mathematics, computer science, or related engineering or science field
(conferred within 3 years prior to employment).  Familiarity with
optimization or deep learning, as evidenced by either completion of a
graduate class that covered optimization or deep learning, or use of
optimization or deep learning in a research setting.  Experience in
numerical methods development in a research setting, with either
released code and/or a published paper on this work.

Due to U.S. export-control laws, only U.S. Persons (U.S. citizens,
lawful permanent residents, asylees, or refugees) are eligible for
consideration.

-------------------------------------------------------

From: Tim Burns [log in to unmask]
Date: May 28, 2021
Subject: Postdoc Positions, NIST NRC

The Applied and Computational Mathematics Division (ACMD) of the
National Institute of Standards and Technology (NIST) invites
applications for two-year NRC postdoctoral research positions at NIST
Laboratories in Gaithersburg, Maryland and Boulder, Colorado. NIST is
a Federal government research laboratory specializing in measurement
science.  ACMD consists of some 46 full-time professional staff, along
with part-time faculty appointees and guest researchers. Staff members
engage in collaborative research with scientists throughout NIST,
providing expertise in applied mathematics, mathematical modeling, and
computational science and engineering.

Research areas of interest include complex systems and networks,
computational materials science, computational fluid dynamics,
computational electromagnetics, computational biology, orthogonal
polynomials and special functions, applied optimization and
simulation, combinatorial software testing, data mining and
visualization, parallel and distributed algorithms, quantum
information science, and uncertainty quantification in scientific
computing.

Candidates and their research proposals are evaluated in a competitive
process managed by the National Research Council (NRC) Associateship
Programs. The current stipend is $72,750 per year; there is also a
$5500 travel and equipment allowance. For further details, see
https://www.nist.gov/itl/math/postdoctoral-opportunities. Application
deadlines are August 1 and February 1. Appointments commence within
one year of selection. For questions, contact Tim Burns,
[log in to unmask]  NIST is an equal opportunity employer. The NRC
Associateship Program at NIST is restricted to US citizens.


-------------------------------------------------------

From: Sebastian Reich [log in to unmask]
Date: May 25, 2021
Subject: PhD/Postdoc Positions, Data Assimilation, Germany

The DFG-funded Collaborative Research Center SFB 1294 "Data
Assimilation - The Seamless Integration of Data and Models", hosted at
the University of Potsdam jointly with its partner institutions HU
Berlin, TU Berlin, WIAS Berlin and GFZ Potsdam, invites applications
for 12 doctoral/academic staff positions (4 years) and 2
postdoctoral/academic staff positions (2 years) in the field of data
assimilation (including statistical inverse problems, dynamical
systems, applied and computational mathematics, and machine learning)
and its application to biophysics, neurosciences, geosciences,
cognative science and pharmacology.

Our vision is to establish a rigorous mathematical underpinning of
data assimilation, to develop principled computational methodologies,
and to apply these methodologies to newly emerging application fields
in the geosciences, neurosciences, pharmacology and biophysics.

The SFB 1294 provides an excellent research infrastructure including a
large interdisciplinary network of researchers and its own graduate
school, as well as funding opportunities for conference visits, summer
schools, and hosting international experts etc. The website
www.sfb1294.de provides more information.

Candidate evaluation will begin immediately after the application
deadline on June 11th, 2021; with an anticipated start of projects on
Septemer 1st, 2021. Interviews will take place between June 25th to
July 2nd. Employment will be made according to the individual
conditions of the participating institutions (University Potsdam, TU
Berlin, HU Berlin, GFZ, WIAS Berlin) employing the selected
candidate. All institutions are equal opportunity / affirmative action
employers. The SFB 1294 seeks to promote diversity in research, and
encourages qualified applicants of any gender and from any background
to apply. Applications need to be submitted via
https://www.geo-x.net/sfb-1294/.


-------------------------------------------------------

From: Thomas Carraro [log in to unmask]
Date: May 29, 2021
Subject: PhD Position, Applied Mathematics, HSU/UniBw, Germany

Helmut Schmidt University / University of the Federal Armed Forces in
Hamburg (Germany) offers a position as PhD student (f/m/d) in Applied
Mathematics.

The candidate will be part of the DFG project "Characterization of
fabrication- microstructure-property relationships for polymer-based
battery materials, combining tomographic 3D imaging with modeling and
simulation". The interdisciplinary project is part of the DFG priority
program "Polymer-based Batteries" (https://www.spp2248.uni-jena.de).
The project focuses on the development of numerical methods for
3D-space-resolved and multiscale electrochemical models.
- Development of high-performance computing techniques based on
  multi-core CPUs and GPUs for solving PDE-based problems on complex
  geometries.
- Develop model order reduction techniques to simulate dynamic
  behavior of batteries.
- Support in the teaching of calculus, linear algebra, and numerical
  mathematics. Courses are taught in German/English.

Please send your application with the usual documents exclusively in
electronic form (PDF file), quoting the reference number MB-2721 by
04.06.2021. You can find the job advertisement at:
https://www.hsu-hh.de/karriere/wissenschaftliches-personal under the
link "Kennziffer MB-2721".

For information on technical questions, please contact
Prof. Dr. Thomas Carraro, e-mail: [log in to unmask]


-------------------------------------------------------

From: Daniele Avitabile [log in to unmask]
Date: May 28, 2021
Subject: PhD Position, UQ for Neuroscience Applications

The Department of Mathematics at the Vrije Universiteit Amsterdam
invites applications for a PhD position in uncertainty quantification
methods for spatially-extended models in neuroscience.

The ideal candidate for this project has a passion for numerical
methods (numerical analysis, scientific computing) and is keen to make
an impact in a novel application for the neurosciences. Previous
knowledge of neuroscience concepts is not a prerequisite, but a firm
interest in learning about neuroscience models and applications is
essential. The project will be supervised by Daniele Avitabile (Vrije
Universiteit Amsterdam) co- supervised by Svetlana Dubinkina (Vrije
Universiteit Amsterdam), and Gabriel Lord (Radboud University), and
myself.

More information can be found at the link below
https://werkenbij.vu.nl/ad/phd-positions-in-mathematical-neuroscience/ngla4t/en

The deadline for applications is the 13th of June 2021. Informal
enquiries can be sent to [log in to unmask]

-------------------------------------------------------

From: Raimondas Ciegis [log in to unmask]
Date: May 26, 2021
Subject: Contents, MMA Journal, 26 (2)

MATHEMATICAL MODELLING AND ANALYSIS
The Baltic Journal on Mathematical Applications, Numerical Analysis
and Differential Equations
ISSN 1392-6292, ISSN 1648-3510 online, Electronical edition:
http://mma.vgtu.lt

Raimondas {\v{C}}iegis (Editor) Volume 26, Issue 2, 2021

CONTENTS

Mohammed Salah Mesai Aoun, Mohamed Selmani and Abdelaziz Azeb Ahmed,
Variational Analysis of a Frictional Contact Problem with Wear and
Damage

Beatriz Campos, Jordi Canela, Antonio Garijo and Pura Vindel, Dynamics
of a Family of Rational Operators of Arbitrary Degree

Swagata Ray, Soumen De and B. N. Mandal, Use of Galerkin Technique to
the Rolling of a Plate in Deep Water

Rupanwita Gayen, Sourav Gupta and Aloknath Chakrabarti, Water Wave
Scattering by a Thin Vertical Submerged Permeable Plate

Islam A. Moneim, An SEIR Model with Infectious Latent and a Periodic
Vaccination Strategy

Erdo\u{g}an \c{S}en and Art\={u}ras \v{S}tikonas, Asymptotic
Distribution of Eigenvalues and Eigenfunctions of a Nonlocal Boundary
Value Problem

Higinio Ramos and Adelegan L. Momoh, Development and Implementation of
a Tenth-Order Hybrid Block Method for Solving Fifth-Order Boundary
Value Problems

Higinio Ramos and Adelegan L. Momoh, Development and Implementation of
a Tenth-Order Hybrid Block Method for Solving Fifth-Order Boundary
Value Problems

Abdeldjalil Chattouh and Khaled Saoudi, Error Analysis of
Legendre-Galerkin Spectral Method for a Parabolic Equation with
Dirichlet-Type Non-Local Boundary Conditions

Andrej Liptaj, Higher Accuracy Order in Differentiation-by-Integration

Mehdi Mesrizadeh and Kamal Shanazari, Meshless Galerkin Method Based
on RBFs and Reproducing Kernel for Quasi-Linear Parabolic Equations
with Dirichlet Boundary Conditions

-------------------------------------------------------
End of Digest
**************************

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November 2018, Week 3
November 2018, Week 2
November 2018, Week 1
October 2018, Week 5
October 2018, Week 3
October 2018, Week 1
September 2018, Week 5
September 2018, Week 4
September 2018, Week 3
September 2018, Week 2
September 2018, Week 1
August 2018, Week 4
August 2018, Week 3
August 2018, Week 2
August 2018, Week 1
July 2018, Week 5
July 2018, Week 4
July 2018, Week 3
July 2018, Week 2
July 2018, Week 1
June 2018, Week 4
June 2018, Week 3
June 2018, Week 2
June 2018, Week 1
May 2018, Week 4
May 2018, Week 3
May 2018, Week 2
May 2018, Week 1
April 2018, Week 5
April 2018, Week 4
April 2018, Week 3
April 2018, Week 2
April 2018, Week 1
March 2018, Week 4
March 2018, Week 3
March 2018, Week 2
March 2018, Week 1
February 2018, Week 4
February 2018, Week 3
February 2018, Week 2
February 2018, Week 1
January 2018, Week 4
January 2018, Week 3
January 2018, Week 2
January 2018, Week 1
December 2017, Week 5
December 2017, Week 4
December 2017, Week 3
December 2017, Week 2
November 2017, Week 4
November 2017, Week 1
October 2017, Week 4
October 2017, Week 3
October 2017, Week 1
September 2017, Week 4
September 2017, Week 3
September 2017, Week 2
September 2017, Week 1
August 2017, Week 4
August 2017, Week 3
July 2017, Week 2
July 2017, Week 1
June 2017, Week 4
June 2017, Week 2
June 2017, Week 1
May 2017, Week 4
May 2017, Week 3
May 2017, Week 2
May 2017, Week 1
April 2017, Week 4
March 2017, Week 4
March 2017, Week 2
March 2017, Week 1
February 2017, Week 4
February 2017, Week 2
February 2017, Week 1
January 2017, Week 3
January 2017, Week 2
December 2016, Week 5
December 2016, Week 3
December 2016, Week 2
December 2016, Week 1
November 2016, Week 3
November 2016, Week 2
November 2016, Week 1
October 2016, Week 4
October 2016, Week 3
October 2016, Week 2
October 2016, Week 1
September 2016, Week 4
September 2016, Week 3
September 2016, Week 2
September 2016, Week 1
August 2016, Week 5
August 2016, Week 4
August 2016, Week 2
August 2016, Week 1
July 2016, Week 4
July 2016, Week 3
July 2016, Week 1
June 2016, Week 4
June 2016, Week 3
June 2016, Week 1
May 2016, Week 5
May 2016, Week 4
May 2016, Week 3
May 2016, Week 2
May 2016, Week 1
April 2016, Week 4
April 2016, Week 3
April 2016, Week 2
April 2016, Week 1
March 2016, Week 5
March 2016, Week 4
March 2016, Week 2
March 2016, Week 1
February 2016, Week 5
February 2016, Week 3
February 2016, Week 1
January 2016, Week 5
January 2016, Week 4
January 2016, Week 3
January 2016, Week 2
January 2016, Week 1
December 2015, Week 5
December 2015, Week 2
November 2015, Week 5
November 2015, Week 4
November 2015, Week 3
November 2015, Week 1
October 2015, Week 4
October 2015, Week 3
October 2015, Week 2
September 2015, Week 5
September 2015, Week 4
September 2015, Week 2
September 2015, Week 1
August 2015, Week 4
August 2015, Week 2
July 2015, Week 5
July 2015, Week 3
July 2015, Week 2
June 2015, Week 5
June 2015, Week 4
June 2015, Week 3
June 2015, Week 2
June 2015, Week 1
May 2015, Week 4
May 2015, Week 3
May 2015, Week 2
May 2015, Week 1
April 2015, Week 4
April 2015, Week 2
April 2015, Week 1
March 2015, Week 5
March 2015, Week 4
March 2015, Week 3
March 2015, Week 2
March 2015, Week 1
February 2015, Week 4
February 2015, Week 3
February 2015, Week 2
February 2015, Week 1
January 2015, Week 4
January 2015, Week 3
January 2015, Week 1
December 2014, Week 5
December 2014, Week 4
December 2014, Week 3
December 2014, Week 2
December 2014, Week 1
November 2014, Week 4
November 2014, Week 3
November 2014, Week 2
October 2014, Week 5
October 2014, Week 4
October 2014, Week 3
October 2014, Week 2
October 2014, Week 1
September 2014, Week 4
September 2014, Week 3
September 2014, Week 2
September 2014, Week 1
August 2014, Week 4
August 2014, Week 3
August 2014, Week 2
August 2014, Week 1
July 2014, Week 5
July 2014, Week 3
July 2014, Week 2
June 2014, Week 5
June 2014, Week 4
June 2014, Week 3
June 2014, Week 2
June 2014, Week 1
May 2014, Week 4
May 2014, Week 3
May 2014, Week 2
May 2014, Week 1
April 2014, Week 4
April 2014, Week 3
April 2014, Week 2
April 2014, Week 1
March 2014, Week 5
March 2014, Week 4
March 2014, Week 3
February 2014, Week 3
February 2014, Week 1
January 2014, Week 3
January 2014, Week 1
December 2013, Week 3
December 2013, Week 2
December 2013, Week 1
November 2013, Week 4
November 2013, Week 3
November 2013, Week 2
November 2013, Week 1
October 2013, Week 4
October 2013, Week 2
October 2013, Week 1
September 2013, Week 4
September 2013, Week 3
September 2013, Week 2
August 2013, Week 4
August 2013, Week 3
July 2013, Week 5

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