Positions for Exceptional Doctoral Students in computer science (deadline August 12, 2019)

Deadline: Tue, 13.08.2019, this call is closed

Helsinki ICT network: Positions for Exceptional Doctoral Students in computer science (deadline August 12, 2019)

The Helsinki Doctoral Education Network in Information and Communications Technology (HICT) is a joint initiative by Aalto University and the University of Helsinki, the two leading universities within this area in Finland. The network involves at present over 60 professors and over 200 doctoral students, and the participating units graduate altogether more than 40 new doctors each year.

The quality of research and education in both HICT universities is world-class, and the education is practically free as there are no tuition fees for doctoral students in the Finnish university system. In terms of the living environment, Helsinki has been ranked as one of the world's top-10 most livable cities (Economist, 2017), and Finland is among the best countries in the world with respect to many quality of life indicators, including being the overall #1 country in human wellbeing. Helsinki is in the second place in the world’s startup city comparison (Valuer, 2018) and is also the Mobile Data Capital of the World (IEEE Spectrum, 2018).

The activities of HICT are structured along five research area specific tracks:

  • Algorithms and machine learning
  • Life science informatics
  • Networks, networked systems and services
  • Software and service engineering and systems
  • User centered and creative technologies

The participating units of HICT have currently funding available for exceptionally qualified doctoral students. We offer the possibility to join world-class research groups, with multiple interesting research projects to choose from. If you wish to be considered as a potential new doctoral student in HICT you can apply to one or a number of doctoral student positions.

We welcome applicants with diverse backgrounds, and qualified female candidates are explicitly encouraged to apply. For more information, please see the list of positions and detailed information on the application process below.

The online application form closes August 12, 2019 at midnight Finnish time.


Autumn call 2019 positions and projects


Project 1: Software Systems for IoT, Edge

Supervisor: Prof. Linh Truong (Department of Computer Science, Aalto University)

The persons hired will research emerging topics of application-specific resource ensembles consisting of IoT, edge and cloud services. The research will develop methods and techniques for edge-cloud resource management, uncertainty testing and management, reliability engineering, elasticity and resilience, dynamic interoperability, and ensemble-aware programming. Research prototypes will be developed for real-world applications and infrastructures. The research will be carried out in connection with possible national and EU research projects.


Project 2: Engineering Analytics of Big Data in IoT, Edge and Clouds Systems

Supervisor: Prof. Linh Truong (Department of Computer Science, Aalto University)

The persons hired will research engineering analytics for big data systems. Especially, the research will focus on developing novel techniques and frameworks for intelligent incident monitoring and analysis, quality of analytics management, and hybrid computing systems for big data. Research prototypes will be developed for real-world applications and infrastructures. The research will be carried out in connection with possible national and EU research projects.


Project 3: Human-guided data analysis

Supervisor: Prof. Kai Puolamäki (Department of Computer Science, University of Helsinki)

The exploratory data analysis group has open a doctoral student position. The topics of interest include the use of randomization and physical simulations to model user’s knowledge and to explore data sets, as well as to make supervised machine learning models - such as deep learning models - more trustworthy and interpretable. Part of the work will be done in collaboration with Institute for Atmospheric and Earth System Research (INAR). Please contact Prof. Kai Puolamäki at kai.puolamaki@helsinki.fi for further information.


Project 4: Analyzing networks of historical correspondence

Supervisors: Prof. Eero Hyvönen and Prof. Mikko Kivelä (Department of Computer Science)

The aim of this project is to use modern temporal network analysis methods to study a large-scale historical communication data set, the Republic of Letters (1500-1800). It consists of information on hundreds of thousands of letters sent during several hundreds of years starting from the 16th century. Similar communication systems have been analyzed before in modern settings with data sets containing up to hundreds of millions communication events via mobile phone calls and emails. This has lead to new kind of information on social patterns in the modern society and how people communicate. Your task will be the first one to use these methods to analyze historical communication patterns and the social structures behind them using the same methods and this newly available data set. Will we see the same patterns emerge in the 16th century correspondence as we do in the modern day messaging? The network analysis tools will be created on top of a SPARQL endpoint based on Semantic Web technologies, creating a new kind of platform for Digital Humanities research.

Requirements: basic knowledge of network science (for example, equivalent knowledge to the Aalto course CS-E5740 Complex Networks). Programming (e.g., Python, JavaScript), data analysis skills, and ability to work with the scale of data used in the project. Enthusiasim for science. The aim is to that the thesis work will lead to a research article published in an international peer-reviewed journal. Considered a plus if you have: any experience on the topics of the project, programming skills in C/C++ and/or JavaScript.

What do you get: Paid position at the Aalto University as a Master's or PhD thesis or worker, access to rich data sets on historical communication and modern communication, a position between two research groups which are world-leading in their fields (network science. Semantic Web, and digital humanities), and international collaborations with, e.g., University of Oxford.

Links: A book describing the underlying Republic of Letters concept and data: https://www.univerlag.uni-goettingen.de/handle/3/isbn-978-3-86395-403-1. Link to a video of a related system on analyzing biographical data: https://vimeo.com/328419960


Project 5: Doctoral student positions at SeCo research group (only for Finnish-speakers)

Supervisor: Prof. Eero Hyvönen (Department of Computer Science, Aalto University)

SeCo research group is looking for new doctoral candidates for three different research projects. The positions are only for Finnish-speaking candidates due to research materials. More information on the projects can be found at:



Project 6: Open doctoral student position in Prof. Antti Oulasvirta’s group – computational methods in HCI

Supervisor: Prof. Antti Oulasvirta (Aalto University, Department of Communications and Networking)

This ERC funded group is looking for PhD students interested in applications of computational methods in HCI. Background and interest in data science, machine learning, modeling, neurosciences, or cognitive science is required. PhD topic will be negotiable. On-going topics in 2019 include, but are not limited to: 1) Interaction techniques for collaborating with an artificial intelligent agent in complex scientific tasks; 2) modeling of input using control theory, neuromechanics; 3) reinforcement learning models of human-computer interaction; 4) computational models of emotion.


Project 7: Federated data sovereignty, semantic interoperability, and data consistency with interledger technologies

Supervisor(s): Prof. Pekka Nikander and Prof. Raimo Kantola (Aalto University, Department of Communications and Networking)

In large federated IoT and information systems, involving tens or hundreds of organisations, the traditional approach to solving trust and security problems has been a separate legal body, acting as a trusted third party.  However, with the introduction of distributed ledger technologies (DLTs, aka blockchains), it has become possible to create new fully decentralised governance models.  When implementing such new governance models, especially for systems that involve IoT, the questions of data sovereignty, semantic interoperability, and federation become crucial.  In this thesis project, these will be considered in a a dynamic and distributed setting involving multiple interconnected blockchains, i.e., in an interledger technology setting.

In that setting, the goal of this thesis project includes the following: a) considering various techniques, such as differential privacy and anonymisation, to protect against privacy attacks, including data mining and correlation attacks, b) analysing ontologies and implementing semantic adapters to enable federation, and c) keeping DLT databases consistent, including transactional atomicity and interledger models for two-phase commit and weak consistency.  The thesis will also involve implementation of the needed software components, requiring strong software development skills.

The proposed Ph.D. thesis will be conducted in the context of the forthcoming EU H2020 project PHOENIX, which involves shielding the grid against complex incidents and extensive cyber and privacy attacks.  In addition to conducting research and studies towards the Ph.D., the selected candidate will also need to act as the task leader for two tasks within the project, collaborating closely with a number of project partners over the Europe. Due to the restrictions in the PHOENIX project, the prospective candidate must be a citizen of a European Union member country.


Project 8: Machine learning for big data analytics and query optimization

Supervisor: Prof. Jiaheng Lu (Department of Computer Science, University of Helsinki)

The topic of the Ph.D. theses will be on big data and machine learning technologies, touching on applying machine learning algorithms for big data analytics and query optimization. Selected candidates will work with the UDBMS research group (http://udbms.cs.helsinki.fi/). 


Project 9:Tools for Bayesian Modeling Workflows

Supervisor: Prof. Aki Vehtari (Department of Computer Science, Aalto University)

We will develop theory and methods for assessing the quality of Monte Carlo and variatonal inference methods, and develop tools for a principled and robust Bayesian modeling workflow. To guarantee wide applicability of the project results in data science industry and academic research, the novel methods will be evaluated on a range of practical machine learning models and implemented as part of the leading open-source probabilistic programming systems.


Project 10: Model Management Systems: Machine Learning meets Database Systems (MLDB)

Supervisor: Prof. Aristides Gionis (Department of Computer Science, Aalto University), Prof. Michael Mathioudakis (Department of Computer Science, University of Helsinki)

Two doctoral positions are available within the MLDB project, funded by the Academy of Finland. The project aspires to develop a computational framework and associated techniques towards model-management systems, i.e., systems that allow users to focus on specifying predictive tasks with limited direct involvement in lower-level decisions for the management of data, models, and computational resources. Requirements: strong analytical and coding skills; master’s level background in machine learning, data management, or related field.


Project 11: Sample-efficient deep learning

Supervisor: Prof. Alexander Ilin (Department of Computer Science, Aalto University)

Deep learning has achieved excellent results in many tasks, such as image classification, speech recognition or statistical machine translation. However, large amounts of data are needed to train a deep learning model with reasonable performance. In this project, we develop deep learning methods that can learn to solve complex tasks with less human supervision, for example, they can learn to classify images from a few training examples (few-shot learning). The key components in our approach is meta-learning and building models with reasoning capabilities.


Project 12: Deep reinforcement learning for AI-assisted design

Supervisor: Prof. Alexander Ilin (Department of Computer Science, Aalto University)

The recent results in reinforcement learning show that computers can outperform humans in solving complex tasks that require planning. For example, computer programs can beat humans in playing complex board or computer games. In this project, we use the potential super-human capabilities of artificial agents to assist humans in solving complex design tasks, for example, designing a floor plan for a new house. The human designer interacts with the agent by setting a set of constraints on the desired design. The artificial assistant learns to propose solutions that satisfy the constraints, look reasonable and are approved by the human designer. Our approach includes building deep generative models of the designs and model-based reinforcement learning to propose new designs.


Project 13: Deep learning of industrial processes

Supervisor: Prof. Alexander Ilin (Department of Computer Science, Aalto University)

Deep learning has become the central element of the state-of-the-art solutions in multiple domains including object detection, speech recognition, statistical machine translation. In this project, we use deep learning to revolutionize the field of industrial process control. We build deep neural models that capture the dynamics of industrial processes and use these models to support humans in complex decision making. We also study ways to combine the knowledge of the physics of an industrial process with the data-driven approach when building deep neural models.


Project 14: Machine Learning for Health

Supervisor: Prof. Pekka Marttinen (Department of Computer Science, Aalto University)

Recent years have witnessed accumulation of massive amounts of health related data, enabling researchers to address problems such as: how to allocate health care resources fairly and efficiently, how to provide personalized guidance and treatment to a user based on time-series data from wearable self-monitoring devices, or how to use genomic data to understand disease or antibiotic resistance. Answering these questions requires new machine learning methodology to be developed. We have several interdisciplinary research projects ongoing, where the goal is to design new machine learning models and algorithms for applications in health and welfare, together with leading experts in the respective fields from Finland and abroad. Examples of applications include: analysis of electronic health records, mobile health, genomics, antibiotic resistance, epidemiology. Successful applicants are expected to have an outstanding record in machine learning, statistics, or a related field, in particular in one or more of the following topics: probabilistic machine learning, deep learning, bioinformatics, Bayesian modeling, causal learning, time-series modeling, likelihood-free inference.


Project 15: Deep generative learning with applications to personalised medicine

Supervisor: Prof. Harri Lähdesmäki, Department of Computer Science, Aalto University

We are looking for a doctoral student to develop probabilistic machine learning and deep generative models for biomedical and health applications. Research projects involve several important clinical challenges, such as personalized immunotherapy and analysis of large-scale biobank data, as well as development of novel machine/deep learning methods. Applicants are expected to have strong background in probabilistic machine learning and programming, and interest in developing/applying probabilistic methods for bioinformatics and biomedicine. For more information and relevant recent publications, see (http://research.cs.aalto.fi/csb/publications) or contact Harri Lähdesmäki (harri.lahdesmaki@aalto.fi).

Project 16: Probabilistic machine learning

Supervisor: Prof. Samuel Kaski (samuel.kaski@aalto.fi) (Department of Computer Science, Aalto University)

We are looking for a student eager to join the Aalto Probabilistic Machine Learning Group to develop new probabilistic models and inference techniques. Particularly promising thesis topics at the moment are Approximate Bayesian Computation (ABC) techniques for inference in simulator-based models, with deep surrogate models and learning the simulators with deep learning methods. But we have a few other equally interesting topics in probabilistic modelling and Bayesian inference, both theory and exciting applications. Contact me for more information!

Links: http://research.cs.aalto.fi/pml/, http://elfi.readthedocs.io


Project 17: Human-in-the-loop machine learning and human-AI collaboration

Supervisor: Prof. Samuel Kaski (samuel.kaski@aalto.fi) (Department of Computer Science, Aalto University

Humans are increasingly interacting with machine learning based adaptive systems, both at work and as consumers. How to optimally combine the strengths of humans and machines is one of the most interesting scientific questions at the moment. We are developing new approaches and applications for interactive human-in-the-loop machine learning and human-AI collaboration - key questions are for instance: how should the AI design its interaction with the human to be maximally useful, what kind of a model would it need to learn of the human for that, and how can it learn the model on-line during the interaction. This project lies at the intersection of machine learning, human-computer interaction, and cognitive science. Relevant machine learning methodologies include reinforcement learning, inference in simulator-based models, interpretable machine learning, probabilistic modelling and programming, and deep learning.

Link: http://research.cs.aalto.fi/pml


Project 18: Privacy-preserving machine learning

Supervisor: Prof. Samuel Kaski (samuel.kaski@aalto.fi) (Department of Computer Science, Aalto University)

Using data in decision making would enable better decisions, but the need to preserve privacy constrains data availability - we have all seen demonstrations how an adversary can recover private information from data analysis results. We develop methods for learning from data such that we can give guarantees that privacy of the data is preserved, using a concept called differential privacy. We have recently introduced ways of doing the learning such that performance actually improves, in contrast to in alternative methods. A couple of “minor” unsolved problems still remain; come solve them with us!

Link: http://research.cs.aalto.fi/pml/


Project 19: Probabilistic machine learning for precision medicine and data-driven healthcare

Supervisor: Prof. Samuel Kaski (samuel.kaski@aalto.fi) (Department of Computer Science, Aalto University)

We are looking for a student to join us in developing new probabilistic modelling and machine learning methods needed for precision medicine, causal inference and predictive modelling based on genomics and clinical data. We combine the ability of modern flexible models to take into account nonlinearities and interactions, with the Bayesian approach which provides a consistent and flexible way to combine available structural information and uncertain observations. In this project, you will develop new probabilistic modeling, Bayesian inference and machine learning methods to make personalized predictions for treatment outcomes, taking into account available side information and structure in the data. The ultimate goal is to personalize medicine both for the patient and the doctor. You will have a chance to collaborate with the machine learning experts in the Finnish Center for Artificial Intelligence (FCAI), as well as with our international collaborators in the top research groups on machine learning and healthcare. Suitable candidates have either a strong background in machine learning and a keen interest to work with top-level medical collaborators to solve these profound medical problems, or strong background in computational biology or medicine, and a keen interest to develop new solutions by working with the probabilistic modelling researchers of the group.

Link: http://research.cs.aalto.fi/pml/


Project 20: Bayesian deep learning

Supervisors: Prof. Samuel Kaski (samuel.kaski@aalto.fi), Dr Markus Heinonen (markus.o.heinonen@aalto.fi) (Department of Computer Science, Aalto University)

We are looking for an eager student to join the Aalto Probabilistic Machine Learning (PML) group to develop novel deep learning techniques from Bayesian perspective. Promising topics include (1) flexible function spaces (deep processes for large-scale data such as images or videos), (2) deep generative models (such as VAEs or GANs), (3) efficient inference for probabilistic neural networks, and (4) deep learning with dynamic models (normalizing flows, neural ODEs). The “deep learning” subgroup  has active research on all these fronts to support the PhD project.

We also have excellent opportunities for applying the techniques across a range of practical problems, for instance in biology, reinforcement learning or human interaction. We utilise the latest techniques such as probabilistic programming, Tensorflow, PyTorch, a GPU cluster, etc.
Requirements: strong background in math, statistics, machine learning or computer science and eagerness to learn the rest.

Link: research.cs.aalto.fi/pml and users.aalto.fi/~heinom10


Project 21: Probabilistic modelling for computational systems biology

Supervisor: Prof. Antti Honkela (Department of Computer Science, University of Helsinki)

The project focuses on developing novel probabilistic modelling and statistical machine learning methodology and applying these methods to problems in computational systems biology. Our aim is to use these methods to understand the regulation of toxin production of the potentially deadly environmental pathogen Clostridium botulinum. The work will be done in close collaboration with biology experts. A successful candidate must have a MSc degree in computer science, electrical engineering, mathematics, statistics, physics, or a related field. A strong mathematical background and an interest in Bayesian modelling and/or machine learning are necessary. An interest in computational biology is essential but no prior experience is necessary.


Project 22: Probabilistic real-time machine learning

Supervisor: Prof. Arno Solin, Department of Computer Science, Aalto University

We are looking for exceptional and highly motivated doctoral students to work on algorithms and applications for real-time machine learning. Central topics and themes in this project include stochastic differential equations, state space modelling, Gaussian processes, and variational inference. Applications of interests are in online decision-making, sensor fusion, audio analysis, control (also linking to RL), and robotics. An intuition of the theoretical backdrop is provided in this NeurIPS paper: https://youtu.be/myCvUT3XGPc and this ICML paper: http://proceedings.mlr.press/v97/wilkinson19a.html.

The work will be done in close collaboration with the supervisor and other members of the team at Aalto University. Doctoral students in the group are encouraged to make research or internship visits to collaborating universities/companies during the course of study. Successful candidates are expected to have completed a Masters degree and have familiarity with machine learning and statistics.

For more information and recent publications and pre-prints, see the research group home page at http://arno.solin.fi


Project 23: Shallow models meet deep vision

Supervisor: Prof. Arno Solin, Department of Computer Science, Aalto University

We are looking for exceptional and highly motivated doctoral students to work on algorithms and methods in combining probabilistic inference and sequential methods with computer vision applications. This topic relates to uncertainty quantification and improving temporal resolution and performance in CV. Central skills are related to deep learning, common computer vision tools and methods, and knowledge of probabilistic methods in ML. This project is part of a consortium project with Tampere University, with collaborators in Oxford, Cambridge, Prague, and Moscow. An intuition of the theoretical backdrop is provided in this paper pre-print: https://aaltoml.github.io/GP-MVS (see also related work on supervisor's home page).

The work will be done in close collaboration with the supervisor and other members of the team at Aalto University. Doctoral students in the group are encouraged to make research or internship visits to collaborating universities/companies during the course of study. Successful candidates are expected to have completed a Masters degree and have familiarity with machine learning and statistics.

For more information and recent publications and pre-prints, see the research group home page at http://arno.solin.fi


Project 24: Theoretical Framework and Deep Learning Algorithms for Large Output Spaces

Supervisor: Prof. Rohit Babbar, Department of Computer Science, Aalto University

There is a growing interest in supervised learning for classification of data with large number of outputs or labels. However, there still lacks good theoretical understanding of algorithms in this domain with only few works such as [1,2]. Unlike other domains, the algorithmic success of deep learning methods for large output spaces has also been somewhat limited. The goal of the PhD thesis would be to explore novel theoretical frameworks and deep architectures in this domain. The scope of the project also includes exploring connections with adversarial robustness analysis of learning algorithms [3,4]. Requirements: Background in Linear Algebra, probability and Optimization. Programming experience with python. Deep learning frameworks such as PyTorch, and working with large datasets. 

[1] Stochastic Negative Mining for Learning with Large Output Spaces, AISTATS 2019

[2] A no-regret generalization of hierarchical softmax to extreme multi-label classification, NIPS 2018

[3] Robustness May Be at Odds with Accuracy, ICLR 2019

[4] Data scarcity, robustness and extreme multi-label classification, Machine Learning Journal, 2019


Project 25: Doctoral student position in computer vision and machine learning

Supervisor: Prof. Juho Kannala (Department of Computer Science, Aalto University)

Computer vision is a rapidly developing field that is at the forefront of recent advances in artificial intelligence. Our group has broad research interests within computer vision. We are pursuing problems both in geometric computer vision (including topics such as visual SLAM, visual-inertial odometry, optical flow, image-based 3D modeling and localization) and in semantic computer vision (including topics such as object detection and recognition, and deep learning). We are looking for students interested in both basic research and applications of computer vision. Students with good programming skills and strong background in mathematics are especially encouraged to apply. The precise topics of the research will be chosen together with the students to match their personal interests.

Examples of our recent papers include: https://aaltovision.github.io/PIVO/https://aaltovision.github.io/pioneer/https://aaltoml.github.io/GP-MVS/
https://arxiv.org/abs/1808.04999 and https://arxiv.org/abs/1810.08393. For more papers and further information visit: https://users.aalto.fi/~kannalj1/


Application process

The Autumn call 2019 includes a number of doctoral student positions in specific research projects by several HICT professors and recruiters. If you wish to be considered as a potential new doctoral student in HICT, you can apply directly to the specific research projects.

An applicant can choose up to 10 projects in the application form (please, mention the project number(s) in the application form). Express your motivation towards the project(s) in your motivation letter (compulsory attachment). You do not have to write several motivation letters in case you apply for multiple projects.

The application form closes on Monday August 12, 2019 at midnight Finnish time, after which applications will be reviewed. Incomplete applications or applications arriving after the deadline will not be considered. Based on the results of the review, top candidates will be invited to interviews.

All the supervisors you indicate on your application form will be informed of your interest, and others also have access to your application documents. If your application is considered strong enough, given the limited resources and intense competition, you will be contacted for a skype interview in August or September 2019.


How are the applications submitted?

Applications need to be submitted through the online electronic application system of Aalto University. Applications sent through any other means will not be processed.


What material is required in the application?

  • Fill in the online application form here (direct link to online application)
  • Attach all required compulsory documents

Note that there are compulsory information/attachments that you need to include in your application (all text documents are to be provided in a single pdf file named "lastname_firstname”.pdf).


Compulsory attachments

Please submit your attachments as single pdf file containing (all documents in English):

  1. Letter of motivation (max. one page) Please describe your background and future plans, and in particular the reasons for selecting the project(s) (you can get more information on the projects and supervisors through their web pages). Try to make your motivation letter as convincing as possible, so that the potential supervisors get interested.
  2. A curriculum vitae and list of publications (with complete study and employment history, please see an example CV at Europass pages)
  3. A study transcript provided by the applicant's university that lists studies completed and grades achieved.
  4. A copy of the M.Sc. degree certificate. If the degree is still pending, then a plan for its completion must be provided.
  5. Contact details of possible referees. Please, provide names, positions, affiliations, and e-mail addresses of 2-3 senior academic people available for providing recommendation letters upon request from HICT. We will contact you and the recommenders separately afterwards, if and when recommendation letters are required. 


Eligibility and required documents later in the recruitment process

In the Finnish university system, a person must have a Master's degree in order to enroll for doctoral studies. In case you wish to pursue graduate studies with a B.Sc. background, please apply first to one of the participating units' Master's programmes (Aalto University SCI or ELEC, and University of Helsinki). A number of these programmes provide special “doctoral tracks” with some financial support and study plans oriented towards continuing to doctoral education after the M.Sc. degree.

In order to get a study right for doctoral studies in Aalto University or University of Helsinki, an applicant with a Master’s degree outside of Aalto University/University of Helsinki needs to meet certain eligibility requirements and present some mandatory documents. For example, an English language proficiency certificate (TOEFL, IELTS, CAE/CPE) is required later in case you will proceed to the recruitment process and apply for a doctoral study right. The universities participating in HICT have strict language skills requirements for doctoral students (Aalto University, University of Helsinki).

Please, be prepared to check the eligibility requirements for doctoral studies and present additional documents in case you will proceed to the recruitment and apply for doctoral study right.

Please, find below more information about the eligibility requirements:

For the Aalto Doctoral Programme in Science (SCI)

For the Aalto Doctoral Programme in Electrical Engineering (ELEC):

For the University of Helsinki


Problems with the application?

First read all the material carefully, and if your problem is still unsolved, only then send email to HICT coordinator: hict-apply@hiit.fi

Please note that HICT coordinator is on holiday in July, we'll get back to you in early August.


General HICT Call FAQ


Who is eligible to apply?

Prospective new doctoral students who are willing to start their doctoral studies in Aalto University or University of Helsinki under one of the HICT supervisors. In order to be successful in the call, the applicant needs to be an exceptional student. Although the number of positions is relatively high, we expect to receive hundreds of good applications, and the process is extremely competitive.

In the Finnish university system, a person must have a Master's degree in order to enroll for doctoral studies. Please, read more information on eligibility above from the call announcement.

Current doctoral students in Aalto University or University of Helsinki cannot participate in this call.

While all applicants who have submitted an application by the deadline will be appropriately considered, Aalto University and the University of Helsinki reserve the right to consider also other candidates for the announced positions.


When does the funding period start?

The exact starting date can be negotiated between the student and the supervisor. The student must have completed his/her M.Sc. degree by the time of starting doctoral studies.

New students also need to go through the standard doctoral student enrolment process of the hosting university/school before the start of the funding period. The supervisors will help in this process, once the best candidates have been identified and linked to a supervisor.


How long is the funding period?

The maximum length of the funding period is four years.


How much is the grant?

The exact amount of monthly salary depends on the stage of the doctoral studies and varies between 2,000 and 3,000 euros/month. The level of the salary is sufficient for a funded student to focus on his/her doctoral studies full-time, without need to resort to other sources of income.


What are the duties and benefits of a doctoral student?

Funded doctoral students are typically hired as full-time employees for the duration of their doctoral studies. The contract includes the normal occupational health benefits of the employing university, and Finland has a comprehensive social security system.

The annual total workload of research and teaching staff at Finnish universities is 1624 hours. In addition to doctoral studies, persons hired are expected to participate in the supervision of students and teaching following the standard practices of the recruiting unit.


Where are the doctoral studies to take place?

Physically the main work location is at the department of the supervisor: in case of the Aalto departments, these are located at the Otaniemi campus while the Department of Computer Science of University of Helsinki is located at the Kumpula Science Campus. The joint research institute HIIT that coordinates the activities of HICT operates on both campuses.

Both campuses are easily reachable from Helsinki city centre by public transportation.

The distance between Otaniemi and Kumpula is only some 10 kilometres, and the participating departments collaborate frequently in research and education (in doctoral education through the HICT network). It is administratively very easy to incorporate courses from the other university as part of a doctoral degree in the other university. HICT will also organise joint seminars, lecture series, workshops and other events for its students.


If I come to study in Helsinki, do I need to learn Finnish?

No: the working environment of doctoral students is highly international, and the working language is English. You can normally also cope in English outside work as most Finns have a very good command of English.

More information for international applicants: