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ALLSTAT  April 2016

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Subject:

JOB: Ph.D. position RTG 2088 "Discovering Structure in Complex Data"

From:

Louisa Ohm <[log in to unmask]>

Reply-To:

Louisa Ohm <[log in to unmask]>

Date:

Thu, 14 Apr 2016 11:18:36 +0100

Content-Type:

text/plain

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Research Training Group RTG 2088 Discovering Structure in Complex Data 


The Research Training Group RTG 2088 “Discovering Structure in Complex Data: Statistics meets Optimization and Inverse Problems” at the Georg-August-University Göttingen offers a

Ph.D. position

for the project A5 “Topological data analysis from a statistical perspective” beginning on August 1, 2016. The salary is in accordance with the German public service salary scale (E13 TV-L) with 75% of the regular working hours (currently 29.85 hours per week) for up to two years.

Topological data analysis aims at a multiscale extraction of topological features in measured (noisy) data. One popular approach is persistent homology, which uses methods from algebraic topology for inferring salient topological features of shapes and functions. Often, persistent homology relies on the assumption that features and noise can be separated in the supremum norm—an assumption that is frequently violated in practice. Indeed, the lack of robustness to outliers is a well-known problem that limits the use of persistent homology without preprocessing the data. In this project we approach topological persistence from the perspective of multi-scale statistics. Our goal is to derive honest confidence statements (in a statistical sense) on topological features without data preprocessing or filtering. Algorithmically, we seek a compromise between statistical efficiency and computational complexity.


Candidates should fulfill the following requirements:

•	A very good M.Sc. degree (or equivalent) in mathematics or similar fields;
•	A strong background in one of the areas of topological data analysis or mathematical statistics is beneficial;
•	Proficiency in written and spoken English;
•	Good programming skills are beneficial.

Please send your application documents (cover letter, CV, university certificates, job certificates, references, etc.) combined as a pdf-file to Diana Sieber ([log in to unmask]).
Detailed information on the research topics and training program of the RTG are available at http://www.uni-goettingen.de/de/514290.html.

The University is an equal opportunities employer and places particular emphasis on fostering career opportunities for women. Qualified women are therefore strongly encouraged to apply as they are underrepresented in this field. 
The University firmly intends to raise the number of disabled persons in its employment. In the case of equally qualified applicants, disabled persons will be preferentially considered.

Closing date is May 31, 2016. For further information please contact
Prof. Dr. Axel Munk ([log in to unmask]) or 
Prof. Dr. Max Wardetzky ([log in to unmask]).

You may leave the list at any time by sending the command

SIGNOFF allstat

to [log in to unmask], leaving the subject line blank.

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