Postdoctoral Researcher in Reinforcement Learning – Københavns Universitet

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Postdoctoral Researcher in Reinforcement Learning

DIKU is offering a 2-year Postdoctoral Fellowship in reinforcement learning expected to commence 1 May 2019 or as soon as possible thereafter. The position is funded by the VILLUM  Grant “Daydreaming Algorithms” held by Associate Professor Stefan Sommer. 
The Daydreaming Algorithms project focuses on incorporating recent neuroscience theories for the functioning of the human brain and the development of cognitive abilities in reinforcement learning. The aim is to use inspiration from neuroscience to broaden the current theoretical setting for reinforcement learning, to use this to enhance the training of reinforcement learning algorithms, and to validate the developed methods experimentally. Deep neural networks will play an important role for achieving this.
The qualified candidate for the postdoc is expected to have a solid theoretical background in machine learning, mathematics, statistics or similar mathematical field. Experience in reinforcement learning is a plus but not a strict requirement. The ideal candidate will be able to take inspiration from developments in fields related to reinforcement learning (mainly neuroscience); to turn this inspiration into advanced mathematical models; to establish and prove properties of these models; and to validate this with computer experiments. Experience in computer implementation of numerical algorithms or machine learning algorithms is a requirement.
The postdoc will join the department’s IMAGE Section ( which hosts experts in fields including machine learning, computer vision and image analysis. The section aspires to be a top research environment that bridges between theory and applications of computer science and mathematical modeling techniques. 
The postdoc’s duties will include research within reinforcement learning. The post may also include performance of other duties.  
Further information on the Department is linked at Inquiries about the position can be made to Associate Professor Stefan Sommer, e-mail:, phone: +45 21 17 91 25.
The University wishes our staff to reflect the diversity of society and thus welcomes applications from all qualified candidates regardless of personal background.


The candidate must have
  • A PhD degree in Computer Science, Mathematics, Statistics or equivalent 
  • A solid publication record in top journals and top conferences within Computer Science, Mathematics, or Statistics (examples being NIPS, ICML, CVPR) 
  • Demonstrated theoretical contributions within machine learning or other mathematical fields 
  • Demonstrated strength within computer implementation of numerical algorithms or machine learning algorithms 

Terms of employment

The position is covered by the Memorandum on Job Structure for Academic Staff.  
Terms of appointment and payment accord to the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State.  
The starting salary is currently up to DKK 426.625 including annual supplement (+ pension up to DKK 72.952). Negotiation for salary supplement is possible.
The application, in English, must be submitted electronically by clicking APPLY NOW below 
Please include  
  • Curriculum vita 
  • Diplomas (Master and PhD degree or equivalent)  
  • Research plan – description of current and future research plans 
  • Complete publication list 
  • Separate reprints of 3 particularly relevant papers 

The deadline for applications is 13 January 2019, 23:59 GMT +1.  

After the expiry of the deadline for applications, the authorized recruitment manager selects applicants for assessment on the advice of the Interview Committee.
You can read about the recruitment process at 

Interviews are expected to be held in early March 2019. 


Part of the International Alliance of Research Universities (IARU), and among Europe’s top-ranking universities, the University of Copenhagen promotes research and teaching of the highest international standard. Rich in tradition and modern in outlook, the University gives students and staff the opportunity to cultivate their talent in an ambitious and informal environment. An effective organisation – with good working conditions and a collaborative work culture – creates the ideal framework for a successful academic career.