GETCO 2018 Conference

Here is a conference announcement:

GETCO 2018
September 10-14, 2018
Oaxaca, Mexico
https://sites.google.com/view/geometricandtopologicalmethods/home
The GETCO conference series focus on applications of algebraic topology in computer science with special emphasis in concurrency, distributed computing, networking and other situations related to systems of sequential computers that communicate with each other. It is aimed at mathematicians and computer scientists working in or interested in these subjects, including researchers and graduate students.

A special issue of the Journal of Applied and Computational Topology will be dedicated to selected papers from the conference.

Committee
Armando Castañeda, Universidad Nacional Autónoma de México
Dmitry Feichtner-Kozlov, University of Bremen.
Eric Goubault, École Polytechnique, Paris.
Maurice Herlihy, Brown University, USA.
Ran Levi, University of Aberdeen.
Martin Raussen, Aalborg University.

Tutorial on Multiparameter Persistence, Computation, and Applications

Matthew Wright and I are organizing a 3-day tutorial, titled "Multiparameter Persistence, Computation, and Applications" at the IMA in Minneapolis, August 13-15.

Details, including a tentative list of speakers, can be found here.

Those interested in participating should apply at the website linked above by March 15.  We expect to be able to provide travel support for at least 20 participants.  Members of groups underrepresented in mathematics are especially encouraged to apply.  Notifications of acceptance and funding will go out by April 1.

3 funded PhD positions in Computational Topology for Materials Science

The 3 PhD positions at the University of Liverpool (UK) are for UK/EU students, though international candidates can contribute to higher tuition fees. Programming skills in C++ or Python are needed. E-mail informal enquiries to vitaliy.kurlin(at)gmail.com

Project 1 : Data Science and Machine Learning Applied to the Discovery of Solid Lithium Ion Conductors. Supervisors: Dr Matthew Dyer and Dr Vitaliy Kurlin. Deadline : 28 February 2018, funded for 3.5 years from October 2018.

Project 2 : Analysis of energy landscapes of molecular crystal structures employing combinatorial and topological methodologies.
Supervisors: Dr Yannis Goulermas, Dr Vitaliy Kurlin, Prof Graeme Day. Deadline : 1 May 2018, funded for 3 years from October 2018.

Project 3 : Data Driven Discovery of Functional Molecular Co-crystals.  Supervisors: Dr Matthew Dyer and Dr Vitaliy Kurlin. Deadline : 31 July 2018, funded for 3.5 years from October 2018.

PhD position in TDA at Swansea University.

As part of the major new Oxford-Swansea-Liverpool Center for Topological Data Analysis (funded by EPSRC grant EP/R018472/1), we are looking for a PhD student to join the Swansea team in investigating applications of topology to data science and physics.

Our team consist of:
Dr Pawel Dlotko - interested in applied and computational topology and data analysis.

Dr Jeffrey Giansiracusa -interested in topology and tropical algebraic geometry.

Prof. Biagio Lucini -interested in computational physics and quantum theory.

A postdoc with research interests in topological data analysis.

Multiple PhD students

The precise focus of the project will be adapted to the interests and background of the successful candidate. Potential emphasis could be in one or more of the following: new computational methods in topological data analysis, algebraic/geometric and category theoretic aspects of topology, topological analysis of phase transitions and topological objects in quantum field theory and other systems.

For more details please consult:
http://www.swansea.ac.uk/maths/postgraduate/phdopportunities/topological-data-analysis/

Do you have any questions? Please let us know:

Jeffrey Giansiracusa (j.h.giansiracusa@swansea.ac.uk.)

Pawel Dlotko (p.t.dlotko@swansea.ac.uk)

Summer School: TDA and Persistent Homology

Topological Data Analysis and Persistent Homology - First Announcement

A school devoted to Topological Data Analysis and Persistent Homology will take place from Monday, June 11 to Friday June 15, 2018, at the Hotel Bellavista in Levico Terme (Trento, Italy), in the framework of the scientific activity of CIRM-FBK (cirm.fbk.eu/).

The program consists of three mini-courses delivered by John Harer (Duke University), Steve Oudot (INRIA Saclay) and Christopher J. Tralie (Duke University).

For details we refer to the official webpage of the school, now published at the link http://www.science.unitn.it/cirm/TDAPH2018.html

Some funds to cover living expenses for young participants are available.

The deadline for registration and application for financial support is April 30, 2018.

PhD position at Tulane in TDA for Digital Pathology

Carola Wenk writes
I have an opening for a PhD position in the use of topological descriptors and HPC methods for digital pathology, see below.

A PhD research assistantship in visualization and computational topology of biomedical data is available in the Department of Computer Science at Tulane University under the supervision of Brian Summa and Carola Wenk, part of the NSF-funded project "Quantifying Morphologic Phenotypes in Prostate Cancer - Developing Topological Descriptors for Machine Learning Algorithms". See here for more information on the project:
http://www.cs.tulane.edu/~carola/research/qubbd.html
This is an interdisciplinary project including faculty and students from Tulane University and Montana State University in the areas of Mathematics, Computer Science, Biomedical Engineering, and Pathology.

The focus of this PhD research assistantship is on the visualization and high-performance computing aspects of topological descriptors computed on gigapixel image data. This research assistantship is for three years of the typically 5-year PhD program in Computer Science, starting in Fall 2018.

For more information and to apply please contact Brian Summa (bsumma@tulane.edu) or Carola Wenk (cwenk@tulane.edu). See here for more information and application instructions for Tulane's Computer Science PhD program: http://www2.tulane.edu/sse/cs/academics/graduate/

Postdoc in TDA at Michigan State University

Elizabeth Munch writes:
I am hiring a postdoc in topological data analysis with an emphasis on Reeb Graphs, mapper, and the interleaving distance to start in the fall of 2018. The successful applicant will have a strong math and/or theoretical computer science background, as well as comfort with programming and implementation.

More information and how to apply can be found here :
https://cmse.msu.edu/employment-opportunities/postdocs/

Of course, feel free to email me with any questions at muncheli@egr.msu.edu.

Job opening in Applied and Computational Topology advertised by the Queen Mary, University of London

From Michael Farber:

Dear colleagues,

I want to attract your attention to the job opening in Applied and Computational Topology advertised by the Queen Mary, University of London (Lecturer/Senior Lecturer in Applied and Computational Topology - QMUL13442), see

https://webapps2.is.qmul.ac.uk/jobs/job.action?jobID=2940

The salary range is £40,865 - £60,109.

Closing date: 11 January, 2018.

PhD studentship at KTH Royal Institute of Technology

https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:181027/type:job/where:4/apply:1

Doctoral student in Machine Learning
KTH Royal Institute of Technology, School of Computer Science and Communication

KTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. We are Sweden’s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as in architecture, industrial management, urban planning, history and philosophy.

Department information

KTH Computer Science and Communication (CSC) announces PhD positions in Machine Learning at the department of Robotics, Perception and Learning (RPL) https://www.kth.se/en/csc/forskning/rpl.

Job description

The scientific work will be conducted along either of the following research directions:

1) Geometric and Topological Methods for Machine Learning with applications to Robotics

Topological Data Analysis is a recently emerging sub-branch of machine learning that enables inference about the global structure of datasets based on rigorous mathematical theory with origins in Algebraic Topology. The research under this theme will focus on developing new geometric and topological techniques for machine learning with a focus on potential application areas in robotics. Possible applications of Geometric and Topological Data Analysis in Robotics include reasoning about robot configuration spaces and free space. This could include development of approaches to autonomously detect unsafe configurations in a self-driving car scenario and to understand how compact components of the free space can be utilized to enable new types of robot manipulation interactions by means of the concept of "Caging". A second potential sub-thread is to investigate how Topological Data Analysis may be of use to analyze representations of data determined by Deep Learning Algorithms. The research will be supervised by Florian Pokorny, Assistant Professor at RPL.

2) Data-driven scene understanding and control in Human-Robot collaborative settings

Future robotic applications are believed to a greater extent include collaboration with humans, humans that do not necessarily have a technical background. Interaction between human and robot thus need to be in a manner that the human finds natural. A collaborative robot should adapt to changes in the environment and tasks that are placed on it. It needs to be in constant learning mode and gradually adjust its behaviours given feedback from both its sensory system and the human collaborator. Research under this theme will focus on a combination of deep learning for scene understanding and generative action modelling, with reinforcement learning applied for control. Emphasis will be placed on methods with which a human can teach a robot the best way of solving a particular task, either through demonstration or by physically guiding the robot. The research will be supervised by Mårten Björkman, Associate Professor at RPL.

This is a four-year time-limited position that can be extended up to a year with the inclusion of a maximum of 20% departmental duties, usually teaching. In order to be employed, you must apply and be accepted as a doctoral student at KTH.

Data science (especially topology position at The Henry M. Jackson Foundation

THE HENRY M. JACKSON FOUNDATION FOR THE ADVANCEMENT OF MILITARY MEDICINE

Position Description
Data Scientist

Position No: FLSA Status: Exempt
Grade: EEO Category/Job Group:

JOB SUMMARY We are seeking a Data Scientist to join the Austere environments Consortium for Enhanced Sepsis Outcomes (ACESO). ACESO aims to identify host-based markers capable of accurately diagnosing and prognosing patients with severe infections in austere settings and transitioning those markers to point-of-care assay platforms. The Data Scientist is responsible for analyzing complex data and developing insights through the use of statistical models, data mining, and data visualization techniques.

This position is based at The Henry M. Jackson Foundation (HJF) in Bethesda, Maryland, although alternate arrangements will be considered. HJF provides scientific, technical and programmatic support services for the worldwide ACESO program.

ESSENTIAL JOB DUTIES: 95% of time

1. Analyzes complex datasets including RNA sequence data, proteomic, phosphoproteomic, and metabolomics data. Applies advanced statistical and predictive modeling techniques and data visualization approaches. Develops innovative approaches to answer research questions.

2. Integrates and prepares large datasets, develops specialized database and computing environments as needed.

3. Provides subject matter expertise as needed, including recommendations on data collection and integration.

4. Communicates results on a regular basis with the science team and key stakeholders, and prepares presentations and reports as needed.

5. Performs other duties as required.

JOB SPECIFICATIONS:
Required Knowledge, Skills, and Abilities:
 Experience with complex datasets
 Proficiency in statistical analysis, forcasting/predictive analytics, and algorithm optimization.
 Experience with data mining/pattern recognition approaches; experience with topological data analysis preferred
 Strong programming skills
 Able to develop solutions to loosely defined problems
 Able to communicate effectively

Minimum Education/Training Requirements: PhD in mathematics, statistics, computer science or related field. At least 2 years relevant experience.

Physical Capabilities: Extended periods of sitting

Required Licenses, Certification or Registration: n/a

Supervisory Responsibilities/Controls: May provide guidance to junior analysts

Work Environment: Office or laboratory environment

Any qualifications to be considered as equivalents, in lieu of stated minimums, require the prior approval of the Director of Human Resources