PhD Position: TDA and Computational Biology

With the framework of the International Training Network CANCERPREV, Kathryn Hess will supervise a PhD student to work on a project concerning “Detection of cyclic changes in gene expression by topological data analysis”.   The student will most probably be affiliated with the new PhD program in computational biology at EPFL in Lausanne (Switzerland).

Interested potential candidates are encouraged to contact Kathryn Hess for more information.  The application deadline is December 15.  Further information about how to apply can be found here

PhD position in TDA/computational biology at EPFL

With the framework of the International Training Network CANCERPREV, I will supervise a PhD student to work on a project concerning “Detection of cyclic changes in gene expression by topological data analysis”. The student will most probably be affiliated with our new PhD program in computational biology.

Interested potential candidates are encouraged to contact me for more information. The application deadline is December 15. Further information about how to apply can be found here.

Special Issue on TDA In MDPI:Algorithms

Dear Colleagues,

We invite you to submit your latest research in the area of applied and computational topology to this Special Issue, “Topological Data Analysis”. We are looking for new and innovative approaches to use methods from algebraic topology in data analysis, statistics, and machine learning. This Special Issue is intended to feature a balance between theoretical developments in topological data analysis and practical applications. Topics include but are not limited to persistence theory, multidimensional persistence, design of filtered simplicial complexes from data, summaries of persistence modules, design of new algorithms to efficiently use topological insights in data science applications, as well as a broad spectrum of applications of topological methods in robotics, biology, medicine, and social sciences. 

For more details, see the website

Nello Blaser
Guest Editor

Giotto-learn, an open-source library for topological machine learning in Python

L2F – Learn to Forecast, the Laboratory for Topology and Neuroscience (EPFL, Switzerland) and the Institute of Reconfigurable and Embedded Systems (heig-vd, Switzerland) are excited to announce Giotto, an open-source project aimed at integrating topological data analysis and machine learning at a fundamental level.

Giotto’s objective is to bring topological data analysis closer to the broader data science community, and to gather contributions from experts in the field.

Our first product is the Python library giotto-learn, released on 21 October 2019 under the Apache 2.0 license. We put an emphasis on making giotto-learn intuitive, user-friendly, and performant. It offers a convenient API and is fully compatible with the most used all-purpose machine learning library in the world, scikit-learn.

giotto-learn inherits the modularity and flexibility of the scikit-learn framework and extends the latter’s reach to include steps inspired by topological data analysis and by the theory of dynamical systems. The ability to create complex pipelines and to use scikit-learn’s model selection and hyperparameter searches, allows for topology-informed machine learning to be performed at larger scales and in the style used in modern data science. Our collaboration’s first paper shows how this allows for an extensive topological analysis of the MNIST digits dataset, including successful classification using topological features only!

While the API is written in Python, the package incorporates compiled C++ code for efficiency. In v0.1.0, Ripser (newly bound to our Python code) is used for fast computation of Vietoris-Rips persistence, and an optimised version of Hera is used for bottleneck and Wasserstein distances.

We look forward to your comments, suggestions and merge requests! Giotto’s core team is happy to help and can be reached at

Postdoc in Applied TDA at INRIA Saclay

In the setting of a collaboration with the “Institut Français du Pétrole et des Energies Nouvelles (IFPEN)” ( ),  the DataShape team at Inria ( )  invites applications for a 3-years researcher position in Topological Data Analysis (TDA).

The research  activities will be mainly driven by concrete practical data science problems. The objectives will focus on the development of new TDA methods and tools, their effective use on problems of interest for IFPEN, and their theoretical analysis.  

We seek outstanding candidates with  research skills in TDA and strong interest for real applications. Good programming skills and some knowledge and experience in statistics and machine learning will be a plus.   

Interested candidates should contact Frédéric Chazal and Marc Glisse by email (  / ) with a detailed CV.      

6th CIMAT Winter School and Workshop in Geometric and Topological Data Analysis

We are pleased to announce the 6th Winter School and Workshop on Geometric and Topological Data Analysis, which will take place at CIMAT Guanajuato in Guanajuato, Mexico, from the 20th-24th of January, 2020.

The program of the event will include the three mini-courses:

Geometric Complexes in Applied Topology
Henry Adams, Colorado State University

Random Cubical Complexes
Erika Roldán, Ohio State University

Algebraic Tools in Multiparameter Persistent Homology
Hal Schenck, Iowa State University

Additionally, there will be research talks on related topics and a poster session. There is still space for contributed talks. If you are interested in giving a talk, please indicate this on the registration form, and send your titles and abstracts to:

Rosy Davalos,

Antonio Rieser,

Limited financial support is available for local expenses for students and early-career researchers.Important dates:

Deadline for titles and abstracts: November 15th, 2019
Deadline for financial support (for students): November 4th, 2019
For registration and more information, please consult the website:
Organizing committee:

Abraham Martín del Campo
Antonio Rieser
Carlos Vargas Obieta
Centro de Investigación en Matemáticas
Jalisco s/n
Col. Valenciana
CP 36023 Guanajuato, GTO

Postdoc in Computational Applied Topology, University of Delaware

The Department of Mathematical Sciences at the University of Delaware invites applications for a postdoctoral researcher position beginning July 1, 2020, supported by the NSF through DMS award 1854683.

The postdoctoral researcher will be a principal member of a collaborative team developing the ExHACT software package for applied/persistent homological algebra and associated theoretical foundations. The researcher will work closely with project PI’s Drs. Chad Giusti (Delaware), Gregory Henselman-Petrusek (Princeton), and Lori Ziegelmeier (Macalester).

The position carries a competitive salary and benefits, and offers interdisciplinary research and training opportunities in applied topology and theoretical neuroscience. Teaching opportunities are available but not required.

Qualifications: Candidates must have completed a Ph.D. in mathematics, computer science, or another relevant field before the position begins. Successful applicants will have a strong background in both algebraic topology and computer programming. Experience with applied topology, C/C++, and software development are desirable.

How to apply: Please apply via Your application must include a cover letter, CV, research statement, and three recommendation letters. Screening of applications will begin on December 01, 2019 and continue until the position is filled.

Informal inquiries are welcome and may be addressed to Dr. Chad Giusti via e-mail at, Dr. Gregory Henselman-Petrusek at, and/or Dr. Lori Ziegelmeier at

The University of Delaware and the ExHACT project team recognize and value the importance of diversity and inclusive excellence in supporting our academic mission and enriching the experience of our employees. We are committed to attracting candidates with varying identities and backgrounds, knowing that diversity enriches the academic experience and expands the knowledge base for innovation. We strongly encourage applications from scholars from under-represented groups. UD provides equal access to, and opportunity in, its programs, facilities, and employment without regard to race, color, creed, religion, national origin, gender, age, marital status, disability, public assistance status, veteran status, sexual orientation, gender identity, or gender expression. The University is responsive to the needs of dual career couples, and supports work-life balance through our family-friendly policies. The University of Delaware is an equal opportunity/affirmative action employer and Title IX institution. For the University’s complete non-discrimination statement, please visit

Postdoc in TDA at the Sorbonne

Julien Thierny and Steve Oudot write:

Postdoctoral position in Topological Data Analysis (TDA) at Sorbonne University, Paris, France (with CNRS and INRIA).

Sorbonne University seeks a postdoctoral researcher in Topological Data Analysis (TDA) under the direction of Julien Tierny (CNRS) and Steve Oudot (INRIA). This position will focus on research projects at the intersection between TDA, data visualization and data science. We are seeking candidates with background in at least one of these areas. The projects will cover questions related to distances between topological representations (algorithms and stability), new computation paradigms for TDA and the applications of TDA to dimension reduction, for a target integration in the Topology ToolKit (

The position is a one-year term (with possibilities for extension) at Sorbonne University, which is located in the center of the city of Paris. Fluency in French is not required.

Please contact us directly (at and for more information.