Department of Computer Science and Engineering, Chalmers University of Technology Company Logo

PhD in Reinforcement Learning, Differential Privacy, or Fairness

 

Available positions

We are looking for a Postdoctoral researcher to join our group at Chalmers University of Technology, Sweden. Deadline 31 May 2022, in the area of reinforcement learning. Go here to apply and for more information

 

We are also looking for a PhD student to join our group on reinforcement learning and decision making under uncertainty more generally, at the University of Neuchatel, Switzerland. We expect the candidate to perform research in one the following domains.

 

Reinforcement learning and decision making under uncertainty:

  • Exploration in reinforcement learning.
  • Decision making nuder partial information.
  • Representations of uncertainty in decision making.
  • Theory of reinforcement learning (e.g. PAC/regret bounds)
  • Bayesian inference and approximate Bayesian methods.
  • Human-AI interaction and inverse reinforcement learning.

 

Social aspects of machine learning

  • Theory of differntial privacy.
  • Algorithms for differentially private machine learning.
  • Algorithms for fairness in machine learning.
  • Interactions between machine learning and game theory.
  • Inference of human models of fairness or privacy.
  • Mechanism design and incentives.

 

The position is fully funded. The main duties include working as a teaching assistant for courses in reinforcement learning or differential privacy and fairness.

The main supervisor will be Christos Dimitrakakis . Examples of our group’s past and current research can be found here. The student will have the opportunity to visit and work with other group members at the University of Oslo, Norway and Chalmers University of Technology, Sweden.

 

The PhD candidate must have a strong technical background, as documented by a degree in statistics, computer science or economics, in the following areas:

  1. Thorough knowledge of calculus and linear algebra.
  2. A good theoretical background in probability and statistics/machine learning.
  3. Practical experience with at least one programming language.

 

The candidate’s background will be mainly assessed through their MSc thesis and transcripts, and secondarily through an interview.

 

Application Information

  • Starting date 1 September 2022 or soon afterwards.
  • Application deadline 31 May 2022.

For the PhD: To apply send an email to christos.dimitrakakis@gmail.com with the subject ‘PhD Neuchatel’.

 

An application must include:

  1. A statement of research interests and motivation relevant to the position.
  2. A CV with a list of references.
  3. Your MSc thesis or another research work demonstrating your academic writing.
  4. A degree transcript.

 

Feel free to include any other additional information.

For the Postdoc: Apply here

 

An application must include:

  1. A statement of research interests and motivation relevant to the position.
  2. A CV with a list of references and publications and a link to your Google Scholar page or similar.

 

Please click here to learn more.

 

No Comments

Sorry, the comment form is closed at this time.