- Faculty of Business Administration - Munich School of Management - Institute of AI in Management
- Start date
- to be agreed
- Application deadline
- 28 Feb 2022
- Remuneration group
- TV-L E13
- Contract duration
- The position is initially for two years with an option to extend.
The Institute of AI in Management at LMU Munich School of Management is seeking for Research and Teaching Assistants (f/m/d) as PhD candidates in Healthcare Analytics.
Our research is driven by the strong desire to conquer the grand challenges of our society: bringing AI into healthcare; curbing COVID-19 fake news with AI tools; fighting the coronavirus epidemic with data-driven insights; automating marketing with AI; and developing new tools for “AI for Good”. For this purpose, we draw upon recent advances in artificial intelligence and statistical modeling in order to produce actionable insights. Different from a PhD in pure computer science or Mathematics, our work is more applied as we tailor advanced analytics to the specific requirements of the application.
Our group, led by Prof. Stefan Feuerriegel, is located in Munich. Further information about our group is available on our website.
In our research, we have partnered with leading institutions – the World Health Organization, the OECD, and leading industry players – to develop new state-of-the-art tools for management that are powered by AI. We further aim to publish a top-tier outlets from applied AI (e.g., KDD, WWW). We are also open to more outlets in technical AI (e.g., AISTATS, ICML), statistics, management (e.g., Marketing Science, Operations Research) or medicine (e.g., Lancet Digital Health, Nature Medicine).
Prof. Stefan Feuerriegel has a dual affiliation with both the LMU Munich School of Management and the Department of Mathematics, Informatics and Statistics at LMU Munich. Because of the latter, we are especially interested in developing new methods.
Tasks and responsibilities
A tentative candidate will be interested in one (or more) of the following topics:
- Machine learning / deep learning / artificial intelligence
- Probabilistic machine learning (hidden Markov models, Hawkes processes)
- Bayesian learning (MCMC, Gaussian processes, variational inferences)
- Causal machine learning
- Policy learning
- Statistics (Bayes)
Our research group is always open to own proposals and further suggestions.
Ideally, the candidate has a Master’s degree in Mathematics, Applied Mathematics, Statistics, Computer Science, or Business Informatics (or a related field). Fluency in the English language is highly recommended. Topic-wise, our research facility is always open to own proposals and further suggestions. Work is almost entirely research-centered with limited teaching duties.
An office which is located at the heart of Munich - most likely Europe's most compelling place to live. You will work with a highly motivated team of aspiring researchers in a very vibrant atmosphere. During this time, participation in conferences and voluntary exchange programs are also supported. Past graduates have landed top-positions in both industry and academia (e.g., University of Oxford). We work together with an international network of researchers (e.g., Harvard, Yale, NYU, Cambridge) to develop solutions for the global challenges of today and tomorrow. Candidates will also gain experience in teaching undergraduate and graduate courses and seminars, and support students in thesis work. The position is initially for two years with an option to extend.
The starting date can be freely chosen. We have scan positions continuously.
We encourage women to apply.
Also possible in a part-time capacity.
People with disabilities who are equally as qualified as other applicants will receive preferential treatment.
Interested? How to apply
We look forward to receiving your online application. We have a lightweight application procedure (CV+transcript only) to minimize effort at both ends. Cover letter or internship reports are not required. Please note that we exclusively accept applications submitted via email and no later than 28.02.2022.
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