PhD Position in Machine Learning Algorithms, KTH Royal Institute of Technology, Sweden

Expires on: 04/30/2024

Join the vibrant research community at KTH Royal Institute of Technology in Stockholm as a PhD student, exploring the foundational theoretical analysis of machine learning algorithms. Under the guidance of Professor Mikael Skoglund and funded by the Swedish Research Council, this project offers an exciting opportunity to delve into the use of information-theoretic tools in understanding and advancing machine learning techniques.

As a PhD student, you will embark on a journey of academic exploration within the dynamic and international research environment of KTH. Under the mentorship of Professor Mikael Skoglund, you will delve into the theoretical underpinnings of machine learning algorithms, with a specific focus on leveraging information-theoretic tools. Collaborate with industry partners and universities worldwide, contributing to cutting-edge research initiatives that push the boundaries of machine learning understanding and application. Additionally, benefit from KTH’s supportive environment, including employee benefits and assistance with relocation to Sweden.


  • Applicants must meet the admission requirements for postgraduate education as per the Swedish Higher Education Ordinance.
  • Basic eligibility criteria include either holding a second-cycle degree (e.g., master’s degree) or completing the necessary course requirements.
  • Proficiency in English equivalent to English B/6 is mandatory.
  • Strong background in theoretical analysis of stochastic phenomena and mathematical analysis of engineered systems is required.
  • Prior experience in information theory is advantageous.

How to Apply:

 Interested candidates should apply through KTH’s recruitment system and ensure that their application includes the following elements:

  1. CV detailing relevant professional experience and knowledge.
  2. Application letter (maximum 2 pages) describing the applicant’s motivation for pursuing research studies, academic interests, and how they align with previous studies and future goals.
  3. Copies of diplomas, grades from previous university studies, and certificates of language requirements fulfillment (translations into English or Swedish if necessary).
  4. Representative publications or technical reports. For longer documents, provide a summary (abstract) and a web link to the full text.

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