Postdoctoral Researcher in Machine Learning for Education

  • EPFL
  • Lausanne, VD, Switzerland
  • 27/08/2021
Full time Data Science Data Engineering Machine Learning Big Data Data Management Statistics

Job Description

EPFL, the Swiss Federal Institute of Technology in Lausanne, is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs more than 6,000 people supporting the three main missions of the institutions: education, research and innovation. The EPFL campus offers an exceptional working environment at the heart of a community of more than 16,000 people, including over 12,000 students and 4,000 researchers from more than 120 different countries.

Postdoctoral Researcher in Machine Learning for Education

Your mission :
The EPFL Machine Learning for Education Laboratory ( headed by Prof. Tanja Käser is looking for a postdoctoral researcher. The lab performs research at the intersection of machine learning, data mining, and education. We are interested in understanding and improving human learning through the creation of accurate models of human behavior and learning. One of the lab’s main focuses is the digital transformation of vocational education and training in Switzerland.As a postdoc, you will develop models and algorithms for representing and predicting student knowledge and behavior across a wide range of domains (e.g. MOOCs, educational games, simulations). Current research topics at the lab include personalization in interactive simulations, game-based learning, reinforcement learning for education, and active and transfer learning. If there is interest, you will also have the possibility to contribute to the development of new simulations and educational games that are being developed in the lab.
Main duties and responsibilities include :

  • Conduct novel research at the intersection of machine learning and education
  • Participate in the supervision of Master’s and Ph.D. students
  • Contribute to applied research projects related to digital vocational education and training
  • Participate in the writing of articles and of other documents related to research activities
  • Participate in the teaching activities of the lab (assistance of exercises, exam correction, supervision of research project students)
  • Support the logistic activities of the lab

Your profile :
Applicants should have completed or be about to complete their Ph.D. in Computer Science or a related technical field. We expect a strong scientific background and proven track record in machine learning/statistics/data mining with an application to educational data. Furthermore, we expect excellent communication skills in English.
When applying, please upload the following documents:

  • Cover letter
  • CV (including a list of publications)
  • 1-2 reference publications
  • Names of two references

We offer :
EPFL ranks among the world’s top universities in computer science, and in machine learning in particular. It is located in Lausanne, Switzerland, a beautiful and vibrant city in an Alpine setting on the shores of scenic Lake Geneva, in the very heart of Europe.EPFL provides an interdisciplinary research setting in digital education, which is unique in Europe. Our lab is a partner of the EPFL Center for Learning Sciences, which brings together multiple initiatives on digital education. EPFL has produced over 80 MOOCs gathering over 2 million registrations worldwide, opened an extension school, sold over 40’000 robots to schools for learning to program, conducted research on technologies for vocational education, eye tracking, HCI, AR, tangibles, analytics, etc. and produced cutting edge research in many sectors of educational technologies. EPFL has also opened an incubator that hosts 70 start-ups in digital education, the Swiss EdTech Collider.

Start date :
October 1st, 2021 or to be agreed

Term of employment :
Fixed-term (CDD)

Duration :
Fixed-Term (CDD) 1-year, renewable

Remark :
Only candidates who applied through EPFL website or our partner Jobup’s website will be considered. Files sent by agencies without a mandate will not be taken into account