About us:

The chair of Statistical Learning and Data Science led by Prof. Dr. Bernd Bischl is a member of the department of statistics at LMU Munich. The chair conducts research broadly in supervised machine learning (ML), e.g., in boosting, random forests and deep learning, but is also specialized on meta-topics like model selection, AutoML and interpretable ML. Prof. Dr. Bernd Bischl is also a director of the Munich Center for Machine Learning (MCML), one of Germany's recently established national competence centers for ML, which will bundle a larger portion of the ML activities at LMU Munich.

We are looking for you:

Research Engineer, Doctoral or Postdoctoral Fellowship Position in Machine Learning for Automated Insurance Tariff Modeling (m/f/x)

in Munich

Your tasks and responsibilities:

This research project is a collaboration of LMU Munich and MSG Life Central Europe GmbH with the main focus on neural architecture search and automated machine learning. In insurance and many other industries, major migrations of data from unmaintainable legacy systems to modern standard software are on the agenda. Such migration projects (e.g., the transfer of actuarial or financial calculations and processing from an old into a new system) are usually very expensive if the underlying data and calculations are laboriously analyzed and programmed manually. For the cost-effective success of these migrations, innovative approaches to extensive automation are urgently required. One of the aims of this project is to use AutoML and meta-learning approaches to assist the automatic migration process and save a lot of expensive and manual effort.

• Active research and publications in the field of AutoML (for PhD and PostDoc positions).
• Developing and improving software implementations of AutoML algorithms.
• Benchmarking and the evaluation of proposed algorithms on data provided by MSG Life Central Europe GmbH.

Your qualifications:

• Degree in computer science, mathematics, statistics, data science or a related discipline
• Experience in applied machine learning
• Experience in hyperparameter optimization (HPO), automated machine learning (AutoML), deep learning and neural architecture search (NAS) is preferred
• Very good programming skills in R and/or Python.
• Motivated personality, eager to learn and ability to work independently
• Team-player, excellent communication and interpersonal skills
• Fluent spoken and written English
• Knowledge in symbolic regression and related techniques is an advantage
• Publication track record in relevant conferences and journals is an advantage
• Background in actuarial sciences is an advantage.

Benefits:

• Fully funded position with option for prolongation
• Cutting edge machine learning projects in an exciting field
• Excellent scientific environment in one of the top-ranked universities in Germany
• International network and exchange opportunities
• Close-knit collaboration with MSG Life Central Europe GmbH, one of the leading insurance software providers for insurance companies.

Also possible in a part-time capacity.

Contact:

• Letter of motivation and your preferred starting date (max. one page).
• Detailed list of all relevant courses you have attended (or taught/assisted) in the field of statistics and machine learning by outlining the main topics covered in these courses (max. 2 pages).
• Detailed CV with a list of publications (if present) and programming skills.
• Certificates and transcript of records of all university degrees obtained.
• For applicants without German proficiency : Please include a certificate of proficiency in English (preferably TOEFL iBT, IELTS Academic, CAE / CPE, PTE Academic or equivalent proof).
• Interested applicants should send all the required documents within one single PDF file (firstname_lastname.pdf) quoting “ RE/Postdoc/PhD Application, AutoML and NAS ” in the email subject line to janek.thomas@stat.uni-muenchen.de

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