Prepare for a career at the forefront of 6G technology with our education programs. Our Education section provides an overview of the courses and research opportunities available for students looking to specialize in 6G. From undergraduate and graduate programs, to research projects and internships, our university offers a wealth of learning experiences for students looking to become leaders in the field. Explore our Education section and learn how you can become part of the next generation of experts in 6G technology.
University Courses

In this University of Helsinki open online course, you learn the fundamental concepts behind mobile networks, focusing in particular on 5G and the envisioned future 6G networks.

Edge intelligence, unlike traditional cloud computing which involves sending data to remote servers for processing, leverages the computing power of devices, such as smartphones, IoT devices, and edge servers, to process data locally to enhance real-time decision-making capabilities, reduce latency, and optimize bandwidth usage. Recently, large language models (LLMs) have achieved great success. However, the scalable training and inference of LLMs are accompanied by a substantial demand for computation resources. We can unlock the power of edge intelligence for LLMs. Meanwhile, LLMs can also empower edge intelligence. The Seminar on Edge Intelligence is a research-focused seminar designed to acquaint participants with various aspects of edge intelligence, providing opportunities for students to engage in academic writing and presentation skills practice.

This course delves into the design, implementation, and challenges of AI systems operating in networked environments. Students will explore various types of networked AI systems, such as distributed AI systems, edge AI systems, multi-agent systems (e.g., reinforcement learning), and federated AI systems. The course covers the evolution of networked AI systems and the intricacies of coordinating and integrating multiple AI components. Emphasis will be placed on practical applications, case studies, and real-world examples, providing students with hands-on experience in designing and deploying networked AI systems. The course also addresses the ethical and societal implications of networked AI systems (e.g., including issues of privacy, security, trust, etc.). Students will learn about the importance of transparency, explainability, and inclusive design in the context of networked AI systems. The course may also feature guest lectures from experts in academia and industry. By the end of the course, students will gain a deep understanding of the challenges and future trends in networked AI systems and will be equipped with the necessary tools and frameworks to implement these systems in various applications.

The course focuses on core networking themes and problems at the higher layers of the protocol stack. Examples of topics we will cover are reliability (e.g., forward error correction), HTTP, RPC, video delivery, fate sharing (as based on the end-to-end argument), and component-based systems (e.g., containers and dockers). Each iteration of the course will focus on 3-4 key themes which will slightly vary from year to year.

The key focus of the course is a problem-based approach to building reliable networked systems and services. We will study the reliability problem at various protocol levels, illustrating how it can be solved at the different levels, and which level might be the most appropriate one for solving the problem. We will also explore the limits of reliability, i.e., just exactly how much can be achieved in practice. The course has a strong practical orientation and requires active participation in the exercises.

Open Positions

The Department of Computer Science, Faculty of Sciences, University of Helsinki invites applications for

University Researcher, computer networks and ubiquitous computing

Position description and requirements: The Department of Computer Science is looking for University Researcher in computer networks and ubiquitous computing with special emphasis on network technologies in 5G and beyond systems, Internet of Things, and cloud technology. The position is related to the Nokia Center for Advanced Research (NCAR) and the Edge AI Special Interest Group (SIG) connecting two Academy of Finland flagships, the Finnish Center for AI (FCAI) and the 6G Flagship. The experimental research work is supported by the cutting edge national 5G Test Network Finland (5GTNF). The University Researcher is expected to conduct top research, lead research tasks and supervise students in computer networking and distributed systems, especially in the area of edge computing, edge AI and 6G. In addition to research work, the University Researcher is expected to commit 5 % (80 hours) of their annual workload to teaching.

The appointment is for five years. The starting date is negotiable but the position will be filled as soon as possible.

The candidate is expected to have a PhD in computer science or other relevant field, strong research track with publications in top venue of the research field, such as the premier ACM and IEEE forums. In addition to research track record, research project management and coordination skills are important for the position as well as ability to provide high-quality teaching based on research. Success in the position requires fluency in English.

The University of Helsinki is committed to fostering an equitable and inclusive working environment and encourages applications from individuals of diverse genders, linguistic, and cultural backgrounds.

Salary and benefits: Salary range for the position is 4300-5000 €/month depending on the qualifications and experience of the applicant.

The University of Helsinki offers comprehensive services to its employees, including occupational health care and health insurance, sports facilities, and opportunities for professional development. The University provides support for internationally recruited employees with their transition to work and life in Finland. For more on the University of Helsinki as an employer, please see

How to apply

Please submit your application in a single PDF file in English, which should include the following documents:

• Curriculum Vitae including the applicant´s scientific, teaching and other relevant merits
• List of publications
• Cover letter describing the candidate´s motivation to apply and suitability for the position

Applications must be submitted through the University of Helsinki´s recruitment portal. Please note that applications or any related materials sent via email will not be considered.

The deadline for applications is April 4th, 2024 (23:59 UTC +3).

Additional information

For more information on the position, please contact Professor Sasu Tarkoma (sasu.tarkoma(at)

For support with the recruitment system, please contact HR Specialist Jussi Hartikainen (jussi.a.hartikainen(at)

The Department of Computer Science of the Faculty of Science is seeking two students to work on a joint project with Nokia Bell Labs (Network Systems and Security Research Lab, Network Operation department). The students can be Master or PhD students looking for an internship, or master's students who would like to do a master's thesis on this topic.

Project description

Efficient processing of continuous data streams is vital for real-time analytics and decisions. Techniques like data pruning and redundancy detection are key to streamlining this process by cutting down on computational and bandwidth demands and reducing the latency. This project aims to evaluate and identify the most effective methods for managing these data streams to improve real-time data processing. Additionally, it explores how tasks should be optimally distributed across different computing resources—such as edge and cloud—to balance resource use, latency, privacy, costs, and system complexity, and explore the various approaches to extract the relevant information that is present in the data streams. The insights gained will be crucial for the development and standardization of future mobile network technologies towards 6G networks.

About the two positions

The positions will require the students to advance research in specific technological areas related to Machine Learning, Artificial Intelligence, and data science approaches for processing and learning from data streams. In particular, the students will be required to carry out a comprehensive state-of-the-art analysis concerning topics selected together with them and develop or extend existing techniques for their thesis, demonstrating the applicability and efficiency of these techniques in determined contexts. While the work of the two students will be complementary, a high level of collaboration between them is essential to ensure the integration of their findings and to maximize the project's overall impact. 

The students will engage in joint efforts with the Nokia Bell Labs research group and a postdoctoral researcher from the University of Helsinki working on the same project.

Students selected for this project will be supervised by Prof. Sasu Tarkoma, alongside a senior researcher or post-doctoral researcher from the University of Helsinki, and Dr. Kimmo Hätönen and Dr. Maryam Sabzevari from Nokia Bell Labs. They will receive a fully funded six-month contract, tailored to their background experience and current level of studies.

Research activities will be conducted at both the Department of Computer Science of the University of Helsinki (Pietari Kalmin katu 5, 00014) and the Nokia Bell Labs offices in Espoo (Karakaari 7, 02610).

Requirements and eligibility criteria

A successful candidate should be enrolled in the Master/PhD of Computer Science or Data Science at the University of Helsinki. Candidates are required to work full-time on this project, meaning suitable candidates should not have commitments to other companies or courses to be given in the forthcoming periods. An exception for a maximum of one course can be considered for exceptional candidates and this can be negotiated with the supervisors.

The project will adopt an agile development methodology and the candidates must possess scientific curiosity, a meticulous work ethic, and the flexibility to work effectively in both academia and industry setups and be ready to divide their presence between university and Nokia Bell Labs offices. In order to excel in this role, familiarity with machine learning (ML), comfortability with the related maths and proficiency in Python are necessary. Also, excellent communication skills in English, both verbal and written, are essential. The University of Helsinki is committed to fostering an equitable and inclusive working environment and encourages applications from individuals of diverse genders, linguistic, and cultural backgrounds.

Salary and Benefits

The salary for the positions will be approximately 2258 euros/month (for master's students) and the salary offered will depend on the students' experience. For PhD students, the salary offered will depend on the students’ experience. The project has the potential for invention reports and patents, and the students will be co-authors of the patents that arise from their contributions. An example of a patent arising from a past collaborations is available here:

How to apply

Please submit your application in a single PDF file in English, which should include the following documents:

  • A motivation letter explaining why you are the ideal candidate for this role, given the context and objectives of the position (max. 1 page).
  • A curriculum vitae (max. 2 pages)
  • If any, a list of publications, especially emphasizing those relevant to the research subject of the position.
  • If any, pointers to source code developed by the student, and contributions to the open-source software community.
  • If any, names and contact information of at least two referees who are willing to provide reference letters upon request or may be contacted directly for further information regarding the candidate's qualifications and experience.
  • Applications must be submitted via email to and


Previous Positions

The "Network Systems and Security Research Lab" at Nokia Bell Labs in Espoo is looking for thesis worker.

In one of the positions, the doctoral student will research, design, and develop optimized software solutions which can be interoperably used in a wide set of smart devices, for performing actionable data-driven insights aimed to ensure optimized sensing and communication tasks in IoT systems. The R&D activities of the other doctoral student will aim to develop an innovative digital twin framework, which can assess, inter alia, efficiency and functionality of the corresponding physical IoT system through the use of AI-powered analytics.

Both positions will also require the doctoral students to advance research in the context of specific technological areas, including, computing and networking resources management, heterogeneous data sources management, AI for IoT networks and systems. The doctoral researchers will be encouraged to design their own research project within the scope of the overall project in collaboration with the PIs. Furthermore, given the scope of the project and the complementarity between the two positions, high cooperation between the two doctoral researchers is a key requirement for a successful outcome of the project activities. The successful candidates will be principally supervised by Prof. Sasu Tarkoma and Dr. Roberto Morabito. The doctoral researchers will be offered up to a 3-year fully funded contract.

For additional information please contact Roberto Morabito (

Mobile devices have emerged as one of the main instruments for tracking movement of people through the use of device-embedded motion sensors. By capturing detailed data about movement, i.e., acceleration and rotation of the device, useful information can be extracted. Thus, this information can be used to recognize physical activities and phenomena and can serve a wide variety of use cases.
For example, the Transportation Mode Discovery (TMD) using mobile devices, i.e., simple motion sensors such as accelerometers, gyroscopes and magnetometers can contribute to a large variety
of applications such as smart mobility and activity tracking.
Currently, detecting different motorized transport modes is difficult and is not supported by standard activity tracking API such as Google’s Activity recognition API. Therefore, in this thesis topic we aim to i) develop state-of-the-art method(s) for TMD, ii) implement the method(s) using existing datasets (or collecting new datasets), and iii) evaluate the performance of the develop method(s) against the existing studies in literature.

The candidate will be employed full time for the duration of six months and will be paid based on the salary system of the University of Helsinki. If you are interested in the topic, for more information please contact Dr. Naser Motlagh (