Postdoc, on the Subject of Data-centric Algorithm Design

Postdoc, on the Subject of Data-centric Algorithm Design

CWI - Centrum Wiskunde en Informatica

Amsterdam, Netherlands

Project description

A key component of machine learning is mathematical optimization, that is used, for example, to train neural networks. The goal of this project is to provide new analysis and tools for optimization problems and algorithms arising in machine learning, but also to use insights and tools from machine learning to improve optimization methods. This explains the project title ‘Optimization for and with machine learning’.

The project consists of four connected work packages. The first two work packages are related to ‘optimization for machine learning’.

  • In the first work package we will investigate why the optimization methods currently used in machine learning are often successful in practice and analyse the limits of their computational tractability.
  • The second work package is aimed at enhancing the existing optimization algorithms and developing new ones to obtain more accurate machine learning models in an efficient way.
  • The last two work packages are related to ‘optimization with machine learning’.
  • The third work package is aimed at using machine learning to obtain data-centric approximation and optimization algorithms. We will develop algorithms that adapt to the specific data characteristics of the problem instance. The advantage of such data-centric algorithms is more accurate solutions and/or less computation time.
  • In the fourth work package we will develop a data-centric optimization modelling approach. In such an approach parts of the resulting optimization model are obtained via machine learning. This data-centric modelling can be used to get more accurate models or can be used in cases where there is no theoretical knowledge available to build the model manually. In addition, we will test our insights on a variety of applications where the consortium members are already involved, including classification problems in the medical sciences, decision problems related to the UN World Food Programme, and routing of shared, self-driving cars.

Job description

The Postdoc will work on topics related to work package 2.

The main objectives will be to investigate and exploit structural properties of data, which can be geometric, algebraic or combinatorial, for the design of dedicated solution approaches. Special areas of focus include (but are not limited to): investigate the use of polynomial functions in machine learning, which links to polynomial optimization, an area that has been extensively studied in the optimization field in recent years; investigate combinatorial algorithms for detecting structural properties of data points, given through their pairwise similarities.

Requirements

Candidates are required to have a completed PhD in the area of Mathematics or Operations Research with a strong mathematical background. A strong affinity with algebraic, combinatorial and geometric methods and with expertise in optimization (semidefinite programming) is required; some background in machine learning is preferred. Needed qualifications for candidates include proven research talent and good academic writing and presentation skills. Candidates are expected to have an excellent command of English.

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© EuroJobsites 2020

EuroJobsites is a registered company number: 4694396 VAT number: GB 880 9055 04

Registered address: EuroJobsites Ltd, Unit 8, Kingsmill Business Park, Kingston Upon Thames, London, KT1 3GZ, United Kingdom

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