PhD position in Automatic Machine Learning (AutoML) (V32.2960)

PhD position in Automatic Machine Learning (AutoML)

We aim to give everyone the power to do great machine learning, and seek a highly creative and motivated PhD student to help us progressively automate the data science process, from raw data to machine learning models, to help everyone do better machine learning, faster.
Position
PhD-student
Departments
Department of Mathematics & Computer Science
FTE
1,0
Date off
31/07/2017
Reference number
V32.2960

Job description

We aim to give everyone the power to do great machine learning, and seek a highly creative and motivated PhD student to help us progressively automate the data science process, from raw data to machine learning models, to help everyone do better machine learning, faster.

Whereas machine learning aims to build systems that are able to learn and improve over time, the creation of such systems is still done largely manually. The data science process contains many tedious, error-prone, or downright painful tasks, such as data wrangling, data preprocessing, model selection, and proper model evaluation. Current approaches to automate these tasks tackle only small parts of this process, often don't work well with raw (or dirty) data and, importantly, they don't always learn much from one problem to the next.

The key research question that we want to answer is how we can (semi)automate the data science process, and how we can learn effectively from one problem to the next. This involves the combination of optimization (model-based optimization, bandits, genetic programming,...) and machine learning, including meta-learning (learning to learn) and deep learning. The end goal is to develop automated processes that use large amounts if prior experiments to build the most promising 'pipelines' of processes for new datasets, evaluate them, and learn from them to propose ever better pipelines. We will leverage OpenML, the open machine learning platform, and develop a series of 'bots' that immediately make the results of this work tangible to the wider data science community and maximize interaction. While certainly challenging, breakthroughs in this area will bring enable many more people to use machine learning effectively, and use it to solve problems important to them.

You will  be collaborating with Dr. ir. Joaquin Vanschoren and the rest of the OpenML core team. This work is set in an very interactive environment, including the Eindhoven Data Mining Group, the OpenML team, and the AutoML community. There is also interaction possible with the US program on 'Data Driven Discovery of Models', and companies working in this area. The availability of extensive (meta)data and expertise offers a unique opportunity for a bright student to tackle this hard problem and become a key player in this field.

Job requirements

We are looking for a motivated candidate with:

  • A Master of Science degree in Computer Science (or similar);
  • Advanced knowledge of machine learning techniques;
  • Strong mathematical and analytical skills;
  • Excellent programming skills. Experience with open source development is an asset;
  • Excellent communication skills in spoken and written English;
  • Creativity, free thinking, perserverance.

Conditions of employment

We offer:

  • Full-time employment as a PhD-candidate for a period of 4 years;
  • Annually 8% holiday allowance and 8.3% end of year allowance;
  • Support with your personal development and career planning including courses, summer schools, conference visits etc.;
  • A broad package of fringe benefits (including an excellent technical infrastructure, child care, moving expenses, savings schemes, coverage of costs of publishing the dissertation and excellent sports facilities)

Information and application

For any further inquiries on the content of the position, please contact Joaquin Vanschoren, e-mail j.vanschoren@tue.nl

For information about employment conditions please contact P. Hertogs LLM, MSc (HR advisor), e-mail: p.hertogs@tue.nl

The application should consist of the following parts:

  • Cover letter explaining your motivation and qualifications for the position;
  • Detailed Curriculum Vitae, including list of publications;
  • Key publications (or links to download).
  • A copy or a link to your Master thesis. If you have not completed it yet, please explain your current situation.
  • A transcript of your grades.

Please apply by using the 'Apply now' button on top of this page.

Applications submitted by e-mail will not be considered.