There are some postings at MathJobs.org that specify Topological Data Analysis. These include:
https://www.mathjobs.org/jobs/jobs/12518 Florida State University; department interests include TDA
https://www.mathjobs.org/jobs/jobs/12021 Several possible sites, including Florida State University where department interests include TDA
https://www.mathjobs.org/jobs/jobs/12350 Hong Kong University, tenured or tenure track
https://www.mathjobs.org/jobs/jobs/12666 Northeastern University, open rank
https://www.mathjobs.org/jobs/jobs/12155 University of Luxembourg, expected to create a new research group
https://www.mathjobs.org/jobs/jobs/12493 Florida State University, tenure track; department interests include TDA
https://www.mathjobs.org/jobs/jobs/12102 North Carolina State University (Raleigh), tenure track
Swansea University in Wales in currently advertising a 2 year postdoctoral research position in topological data analysis.
As part of the major EPSRC-funded Oxford-Swansea-Liverpool Centre for Topological Data Analysis, we are looking for a postdoctoral researcher to join the Swansea team in investigating applications of topology to data science and physics. The Swansea team consists of Pawel Dlotko, Jeff Giansiracusa, Biagio Lucini, and PhD students.
We are particularly interested in applicants with background in any of these areas:
* Computational topology / geometry
* Computational physics and lattice gauge theory
* Data science or machine learning
The ideal candidate will have some computer coding experience and be willing to learn new things.
Full details and application instruction can be found here:
Salary: from £33,518 to £38,833
Closing date: 4 November 2018
Pawel Dlotko ( P.T.Dlotko@swansea.ac.uk ) or
Jeff Giansiracusa ( firstname.lastname@example.org )
if you are interested.
We are happy to announce an ICERM topical workshop on “Applied Mathematical Modeling with Topological Techniques”, August 5-9, 2019. Please see our webpage
Mathematical modelers face a variety of challenges, including summarizing large data sets to understand and explore a system of interest, inferring the model parameters most accurate for describing a given data set, and assessing the goodness-of-fit between data sets. Computational topology provides a lens through which these challenges may be addressed. At the same time, just as topological techniques provide opportunities for modelers, the challenges that modelers face give rise to opportunities for applied topologists. For instance, topologists may develop techniques that make model predictions based on the topology of experimental or simulation data, that analyze time-varying data, or that turn model outputs into formats suitable for machine learning.
This workshop brings together the applied mathematical modeling and applied topology communities, aiming to give modelers exposure to topological techniques still not commonly used in their community, and to give topologists exposure to modeling challenges that might stimulate the development of new techniques.
The workshop will include tutorial sessions on modeling and on applied topology, during which participants will learn by doing hands-on computational exercises. Because the broad goal of this workshop is to encourage collaboration between members of the applied modeling and topology communities, a significant portion of the week will be devoted to participants initiating research on problems proposed by the organizers. The research problems will afford the potential for continued collaboration beyond the workshop.
Henry Adams, Maria D’Orsogna, Rachel Neville, Jose Perea, Chad Topaz