Positions for Doctoral Students in computer science (deadline August 17, 2020)

Deadline: Mon, 17.08.2020, this call is open


Helsinki ICT network: Doctoral student positions in computer science (deadline August 17, 2020)

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 is also in the third place in the world’s startup city comparison (Valuer, 2019). Finland is among the best countries in the world with respect to many quality of life indicators, ranking at the top in education and subjective wellbeing. Furthermore, Finland is again the world's happiest country in 2020 (World Happiness Report, 2020).

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 over 30 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 17, 2020 at midnight Finnish time.

 

Autumn call 2020 positions and projects

 

Project 1: Human-guided data analysis in natural sciences

Supervisor: Assoc. Prof. Kai Puolamäki (Department of Computer Science and Institute for Atmospheric and Earth System Research (INAR), University of Helsinki)

The exploratory data analysis group is looking for a doctoral student for a project that focuses on the use of artificial intelligence and machine learning on natural sciences, especially in atmospheric and earth sciences. The objective is to study probabilistic causal models of measured and simulated physical phenomena and to study the methods that would enable the substance area experts to build these models. The project can be tailored to focus more on computer science or atmospheric sciences, depending on qualifications and preferences of the applicant. The project will be done in collaboration with the Institute for Atmospheric and Earth System Research (INAR) at the University of Helsinki. Please contact Prof. Kai Puolamäki at kai.puolamaki@helsinki.fi for further information.

 

Project 2: Simulation-based Design of Personalized Wearables

Supervisor:  Prof. Yu Xiao (yu.xiao@aalto.fi), Aalto/COMNET

The student will participate in a multidisciplinary research project titled personalised virtual stroke rehabilitation project from the beginning of 2021, and will conduct research on simulation-based design of personalised smart gloves for virtual stroke rehabilitation. The smart gloves made of e-textiles are supposed to support precise finger positioning, fine-grained human-object interaction detection, and haptic interaction with virtual reality. The student will contribute to the development of a design toolkit that optimizes the selection and spatial distribution of sensor modalities, generates synthetic sensor data using physical simulators, and trains customized machine learning models using synthetic sensor data.  More information about the research group is available at mobilecloud.aalto.fi.

The student is expected to have a master degree in computer science, robotic engineering, or electrical engineering, and to have basic knowledge about machine learning and strong programming skills. Experience with electronic design or interaction design is considered as a plus.

In addition to CV, a research plan and recommendation letters from previous supervisors are expected.

 

Project 3: HAIC: Open doctoral student position in Prof. Janne Lindqvist’s group – security engineering and usable security

Supervisor: Prof. Janne Lindqvist (Department of Computer Science, Aalto University), Director Helsinki-Aalto Center for Information Security (HAIC)

We are looking for PhD students interested in security engineering, usable security and human-computer interaction. Background and interest in systems security, security engineering, data science, machine learning, modeling, human-computer interaction or social and behavioral sciences is required. PhD topic will be agreed together with the applicant. Examples of work done in the group can be found at old website for the group https://www.lindqvistlab.org/. The students will also get to participate in the activities of HAIC. Please contact me at the aalto.fi email address about these positions.

 

Project 4: Open doctoral student position in Prof. Janne Lindqvist’s group – understanding video streaming user experiences

Supervisor: Prof. Janne Lindqvist (Department of Computer Science, Aalto University), Director Helsinki-Aalto Center for Information Security (HAIC)

We are looking for doctoral students interested in understanding video streaming user experiences. Background and interest in measuring user experience, modeling, human-computer interaction, computer science or social and behavioral sciences is required.  Examples of work done in the group can be found at the old website for the group https://www.lindqvistlab.org/. Please contact me at the aalto.fi email address about these positions.

 

Project 5: HAIC: Open doctoral student position in Prof. Janne Lindqvist’s group – artificial intelligence and machine learning for systems security and privacy

Supervisor: Prof. Janne Lindqvist (Department of Computer Science, Aalto University), Director Helsinki-Aalto Center for Information Security (HAIC)

We are looking for doctoral students interested in developing novel artificial intelligence and machine learning approaches to security engineering and systems security and privacy.. Background and interest in data science, machine learning, statistics and computational approaches to computer science are required. Examples of work done in the group can be found at the old website for the group https://www.lindqvistlab.org/. The students will also get to participate in the activities of HAIC. Please see specific examples also http://jannelindqvist.com/publications/IMWUT19-fails.pdf http://jannelindqvist.com/publications/NDSS19-robustmetrics.pdf  Please contact me at the aalto.fi email address about these positions.

 

Project 6: Open doctoral student position in Prof. Janne Lindqvist’s group – multitasking and productivity tools

Supervisor: Prof. Janne Lindqvist (Department of Computer Science, Aalto University), Director Helsinki-Aalto Center for Information Security (HAIC)

We are looking for doctoral students interested in understanding productivity tools and multitasking. Background and interest in measuring user experience, modeling, human-computer interaction, computer science or social and behavioral sciences is required.  Examples of work done in the group can be found at the old website for the group https://www.lindqvistlab.org/. Please contact me at the aalto.fi email address about these positions.

 

Project 7: Open doctoral student position in Prof. Janne Lindqvist’s group – mixed methods HCI and security research

Supervisor: Prof. Janne Lindqvist (Department of Computer Science, Aalto University), Director Helsinki-Aalto Center for Information Security (HAIC)

We are looking for doctoral students interested in pushing the envelope in mixed methods HCI and security research. Background and interest in measuring either qualitative methods or quantitative methods, and interested to learning new methods, user experience, modeling, human-computer interaction, computer science or social and behavioral sciences is required.  Examples of work done in the group can be found at the old website for the group https://www.lindqvistlab.org/. Please contact me at the aalto.fi email address about these positions.

 

Project 8: Machine learning and differential privacy

Supervisor: Associate Professor Antti Honkela (Department of Computer Science, University of Helsinki)

Differential privacy allows developing machine learning algorithms with strong privacy guarantees. In this project, you will join our group in developing new learning methods operating under these guarantees. Our work covers both Bayesian machine learning and deep learning. The project combines theory and practice and requires a strong background in mathematics.
More information and papers: https://www.cs.helsinki.fi/u/ahonkela/

 

Project 9: Machine Learning for Health (ML4H)

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

Recent years have witnessed an accumulation of massive amounts of health related data, enabling researchers to address diverse problems such as: how to allocate healthcare resources fairly and efficiently, how to provide personalized guidance and treatment to users based on real-time data from wearable self-monitoring devices, or how to use genomic data to understand disease or antibiotic resistance. Central challenges in ML4H include analysing massive amounts of diverse data from multiple data sources, going beyond correlation to learn about causal relations between relevant variables, interpreting the models, and assessing the uncertainty of predictions, to name a few. We tackle these by developing new models and algorithms which leverage modern principles of machine learning, using techniques such as deep neural networks, probabilistic methods, interactive machine learning, attention, and generative models. Examples of our ongoing interdisciplinary projects include: analysis of nationwide healthcare register data, mobile health, genomics, antibiotic resistance, and epidemiology. Successful applicants are expected to have an outstanding record in machine learning, statistics, applied mathematics, or a related field, and a passion to put these skills to use in interdisciplinary research to address some of the most burning challenges in today’s society.

 

Project 10: 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 approximate inference methods, stochastic differential equations, state space modelling, and Gaussian processes. Applications of interests are in online decision-making, sensor fusion, audio/video 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 11: Deep learning with probabilistic principles

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 Cambridge, Oxford, Prague, and Moscow. Examples of recent work in the group on this topic: https://aaltoml.github.io/GP-MVS and https://aaltoml.github.io/view-aware-inference/.

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 12: Mobile Cross Reality through Immersive Computing (MeXICO)

Supervisors: Prof. Mario Di Francesco, Matti Siekkinen (Department of Computer Science, Aalto University)

The MeXICO project aims to overcome the limited resources of mobile devices for cross-reality (XR): virtual, augmented, and mixed reality. Indeed, current solutions for interactive mobile XR that require real-time graphics rendering are constrained by the limited graphics processing capabilities of mobile devices. A promising approach to overcome this issue is to offload most of the heavy graphics computing from the mobile device to a remote graphical processing unit in the cloud. Unfortunately, this introduces two major challenges: latency and bandwidth. In fact, a noticeable motion-to-photon latency with highly interactive applications negatively affects user experience and may even cause severe discomfort. Moreover, transmitting high-quality graphics from remote servers to mobile devices requires a large amount of network bandwidth. MeXICO explores solutions to cope with these challenges of mobile and distributed XR through three main research directions:

distributed and collaborative game engine and rendering; methods to hide long and variable latency for XR with six degrees of freedom; and seamless bitrate adaptation and graphics foveation with highly-varying bandwidth.

 

Project 13: Association mining algorithms for biomarker discovery

Supervisor: Wilhelmiina Hämäläinen (Department of Computer Science, Aalto University)

Biomarker discovery is an essential tool for understanding disease mechanisms, diagnosing diseases, and predicting progression of diseases or efficacy of treatments. Often the new information can be presented as association patterns between sets of biomarkers (genes, transcripts, proteins, metabolites, etc.) and conditions (like tumour type or risk of disease). In this doctoral research project, the goal is to develop and apply statistical association rule discovery algorithms and related post-processing methods on omics-based biomarker data discovery. The precise research topic will be defined together with the student according to her/his interests.

Motivated, algorithmically talented students with sufficient knowledge on data mining and good programming skills (especially C/C++ or Java) are encouraged to apply. Prior knowledge on statistics and bioinformatics are very useful but not necessary prerequisites.

 

Project 14: Machine learning models of human behavior

Supervisor: Associate Prof Antti Oulasvirta / School of Electrical Engineering & the Finnish Center for Artificial Intelligence; Homepage: http://users.comnet.aalto.fi/oulasvir/

A second supervisor within FCAI is possible (see www.fcai.fi)

The Finnish Center for AI’s goal is “Real AI”, i.e. AI that can work with people, helping them in complex activities such as design and decision-making, and augmenting their capabilities. Such AI needs to understand people somewhat like people do, inferring factors behind observable behavior, planning sensitively with possible consequences in mind, and providing common ground for collaboration. We look for outstanding machine learning or cognitive science students to join our efforts to build machine learning models of human behavior. Such models must be updateable with available -- and often impoverished, noisy -- observational data, serve as basis of actively probing (experimenting) with the world to learn about it, and predict the consequences of its action on its partners.

 

Project 15: Computer Assisted Bayesian Modeling Workflow

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

Short task description for applicants, including necessary skills / prerequisites (half-­‐page maximum):

You will participate in a research project in which we will develop theory and methods for a principled and robust computer assisted 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. Prerequisite is knowledge Bayesian methods and some probabilistic programming. Experience with Stan is preferred but not mandatory.

 

Project 16: Probabilistic machine learning

Supervisor: Prof. Samuel Kaski (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 (1) Bayesian reinforcement learning and inverse reinforcement learning, especially in multi-agent settings, (2) Bayesian deep learning, (3) distributed and federated inference, and (3) simulator-based inference: how to combine first-principles models with learning from data. We have a few other exciting topics as well; contact me for more information!

Links: https://research.cs.aalto.fi/pml/research.shtml

 

Project 17: Machine learning with humans

Supervisor: Prof. Samuel Kaski (Department of Computer Science, Aalto University)

Most machine learning systems operate with us humans, to augment our skills and assist us in our tasks. In environments containing human users or, more generally, intelligent agents with their goals and plans, the system can only help them reach those goals if it understands them. We develop the probabilistic interactive user models and inference techniques needed to learn to understand other agents and interact with them more efficiently. Additional keywords: active learning, automatic experimental design, knowledge elicitation, multi-agent learning, machine teaching, inverse reinforcement learning.

Links: https://research.cs.aalto.fi/pml/

 

Project 18: Probabilistic modelling for personalized medicine and drug development

Supervisor: Prof. Samuel Kaski (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 in the core problems of modern healthcare: developing better drugs and personalizing treatments. For both problems, we combine the ability of modern flexible models to take into account nonlinearities and interactions, with the Bayesian approach to handle the uncertainty in the data and results. Precision medicine needs causal inference and predictive modelling based on genomic and clinical data, and drug development additionally generative models of chemistry; both need adaptive experimental design. This is an excellent opportunity to work with top-notch experts in both medicine (cancer and clinical) and machine learning.

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

 

Project 19: Algorithms and Data Structures for GPUs

Supervisor: Prof. Simon J. Puglisi (Department of Computer Science, University of Helsinki)

GPUs have become commonplace and, outside their original aim as a tool for graphics processing, have gained wide use in machine learning. The general aim of this project is to explore the use of GPUs in other data intensive problems via the design and implementation of efficient GPU-based algorithms (and associated data structures) for sequential data processing and data compression. The applicant should have a strong interest in algorithms and data structures and not be scared of developing code in C++. Knowledge of/experience with CUDA is an advantage, though not at all required.

 

Project 20: Doctoral student position in Prof. Jaakko Lehtinen's group

Supervisor: Prof. Jaakko Lehtinen (Department of Computer Science, Aalto University)

We are looking for a doctoral student to join our group. Our work is in the exciting intersection of computer graphics, computer vision, machine learning, and artificial intelligence. The topic of the dissertation project can be discussed and agreed together. Link: https://users.aalto.fi/~lehtinj7/

 

FCAI positions within the HICT Autumn call 2020 (Projects 21-33)

Finnish Center for Artificial Intelligence FCAI is a community of experts that brings together top talents in academia, industry and public sector to solve real-life problems using both existing and novel AI.

FCAI’s research mission is to create a new type of AI that is data efficient, trustworthy, and understandable. FCAI research is advanced in seven Research Programs (1–7) and five Highlights (A–E) with the common methodological goal to build AI systems capable of helping their users in AI-assisted design, decision-making and modelling. The common methodological goal ensures that the Research Programs build towards FCAI’s research mission while the Highlights exist to make sure the fundamental research in FCAI Research Programs is taken into use. Multiple research groups are involved in each program.

In this call, you can apply to work in one or more of the FCAI Research Programs, Highlights and/or in the common methodological goal. We primarily seek applicants to work in multiple programs with joint supervision of two professors.

For more detailed information, please see https://fcai.fi/research/.

 

Project 21: FCAI’s common methodological goal: AI’s for AI-assisted design, decision-making and modelling

FCAI’s common methodological goal: AI’s for AI-assisted design, decision-making and modelling. We develop AI techniques needed for systems which can help their users make better decisions and design better solutions across a range of tasks from personalized medicine to materials design.  A core insight in developing such AIs is that they need to have world models for understanding the world and interacting with it, and user models for understanding the user and interacting with them.

Keywords: automatic experimental design, interactive user modelling, machine teaching, sequential decision making, causal inference, counterfactual inference, digital twins

Coordinating professor: FCAI Director Samuel Kaski (Aalto University). All programs (1–7; A–E) contribute.

 

Project 22: FCAI Research Program 1: Agile probabilistic AI

Agile probabilistic AI develops an interactive and AI-assisted process for building new AI models with practical probabilistic programming. Read more at https://fcai.fi/agile-probabilistic

Keywords: Probabilistic programming; robust and automated Bayesian machine learning.

Coordinating professor: Aki Vehtari (Aalto University). Several professors contribute to the program.

 

Project 23: FCAI Research Program 2: Simulator-based inference

Simulator-based inference develops methodology for the new AI having efficient, interpretable reasoning capability, by cross-breeding modern machine learning and simulator-based inference. Read more at https://fcai.fi/simulator-based

Keywords: Approximate Bayesian computation ABC; likelihood-free inference; generative adversarial networks (GAN); applications in many fields including medicine, materials design, visualization, and business.

Coordinating professor: Jukka Corander (University of Helsinki). Several professors contribute to the program.

 

Project 24: FCAI Research Program 3: Next generation data-efficient deep learning

Next generation data-efficient deep learning develops methods which harness the power of deep learning while achieving good results with less training data and in particular less human supervision. Read more at https://fcai.fi/deep-learning

Keywords: Deep reinforcement learning; semi-supervised learning; simulation methods; Bayesian deep learning.

Coordinating professor: Arno Solin (Aalto University). Several professors contribute to the program.

 

Project 25: FCAI Research Program 4: Privacy-preserving and secure AI

Privacy-preserving and secure AI develops the new principles and techniques needed for privacy-preserving machine learning and the tools for building trustworthy and secure AI systems. Read more at https://fcai.fi/privacy-preserving-and-secure

Keywords: Privacy-preserving machine learning; differential privacy; adversarial machine learning.

Coordinating professor: Antti Honkela (University of Helsinki). Several professors contribute to the program.

 

Project 26: FCAI Research Program 5: Interactive AI

FCAI Research Program 5: Interactive AI enables AI that people can naturally work and solve problems with, and which demonstrates the ability to better understand our goals and abilities, takes initiative more sensitively, aligns its objectives with us, and supports us. Read more at https://fcai.fi/interactive-ai

Keywords: Interactive machine learning; reinforcement learning and computational rationality; cognitive modelling; probabilistic programming for behavioral sciences.

Coordinating professor: Antti Oulasvirta (Aalto University). Several professors contribute to the program.

 

Project 27: FCAI Research Program 6: Autonomous AI

FCAI Research Program 6: Autonomous AI addresses the fundamental challenges of long-term autonomous operation, in particular, how learning and planning can be performed to ensure safe operation over long time horizons. Read more at https://fcai.fi/autonomous-ai

Keywords: Autonomous systems; Reinforcement learning; Model predictive control

Coordinating professor: Ville Kyrki (Aalto University). Several professors contribute to the program.

 

Project 28: FCAI Research Program 7: AI in society

FCAI Research Program 7: AI in society focuses on social and ethical dimensions of AI. It deals both with the preconditions of trustworthy and socially acceptable AI and the consequences of uses of AI. It aims to bring together AI research and human sciences to better understand how AI works in organizations and society. Read more at https://fcai.fi/ai-in-society

Keywords: Design and domestication of AI; Understandability of AI; Foresight and responsibility in AI decision-making and robotics; Legitimacy and social acceptability of AI

Coordinating professor: Petri Ylikoski (University of Helsinki). Several professors contribute to the program.

 

Project 29: FCAI Highlight A: Easy and privacy-preserving modeling tools

FCAI Highlight A: Easy and privacy-preserving modeling tools has the main objective to measure and maximize the impact of FCAI research on the process of probabilistic AI development. Read more at https://fcai.fi/modeling-tools

Coordinating professor: Arto Klami (University of Helsinki). Several professors contribute to the program.

 

Project 30: FCAI Highlight B: Applications of AI in healthcare

FCAI Highlight B: Applications of AI in healthcare creates AI tools to tackle real-world problems in healthcare together with expert collaborators from the respective fields. Read more at https://fcai.fi/ai-healthcare

Application 1: AI for genetics
Application 2: Computational design of vaccines
Application 3: Deep learning for healthcare resource allocation

Coordinating professor: Pekka Marttinen (Aalto University). Several professors contribute to the program.

 

Project 31: FCAI Highlight C: Intelligent service assistant for people in Finland

FCAI Highlight C: Intelligent service assistant for people in Finland has a mission to deploy real AI services for wide audience in Finland. The Highlight is directly linked to AuroraAI initiative (https://vm.fi/auroraai). Read more at https://fcai.fi/service-assistant

Coordinating professor: Tommi Mikkonen (University of Helsinki). Several professors contribute to the program.

 

Project 32: FCAI Highlight D: Intelligent urban environment

FCAI Highlight D: Intelligent urban environment focuses on how to combine (i) measurements from natural environment, (ii) simulations, and (iii) modeling in order to, e.g., make decisions in interaction with the user (e.g., "what-if-engines") and to model and understand observed and/or simulated processes. This includes applications of Interactive AI, Agile probabilistic AI and simulators to model measurements and simulator outputs from urban environments. Read more at https://fcai.fi/intelligent-environment

Coordinating professor: Kai Puolamäki (University of Helsinki). Several professors contribute to the program.

 

Project 33: FCAI Highlight E: AI-driven design of materials

FCAI Highlight E: AI-driven design of materials develops AI technology for accelerated materials design and characterization. Read more at https://fcai.fi/ai-materials

Coordinating professor: Patrick Rinke (Aalto University). Several professors contribute to the program.

 

Application process

The Autumn call 2020 includes a number of doctoral student positions in specific research projects by several HICT professors and researchers. 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, but if you prefer you can attach separate letters for individual projects.

The application form closes on August 17, 2020 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 2020.

 

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?

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. You do not have to write several motivation letters in case you apply for multiple projects, but if you prefer you can attach separate letters for individual projects.
  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. (The letter describing the completion plan can be free-format.)
  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 School of Science (SCI) or School of Electrical Engineering (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. A successful applicant must have an excellent command of Finnish, Swedish, or English. The universities participating in HICT have strict language skills requirements for doctoral students (Aalto University, University of Helsinki). All international applicants applying for doctoral studies must demonstrate their proficiency in English. 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. Only the following applicant groups can be exempted from the language test requirement: applicants who have completed a higher education degree 1) taught in Finnish, Swedish or English in a higher education institution in Finland or 2) in an English-medium programme at a higher education institution in an EU/EEA country, provided that all parts of the degree were completed in English or 3) an English-medium higher education degree requiring a physical on-site presence at a higher education institution in the United States, Canada, Great Britain, Ireland, Australia or New Zealand. More information on minimum language requirements and language test scores can be found at Master programmes admission webpage (see "Language requirements" and "demonstrating proficiency in English").

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 in Aalto University or University of Helsinki.

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

Please note that our reply may take longer than usual due to summer holidays.

 

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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.

Important about COVID-19: Please, note that the coronavirus situation may constrain recruitments from abroad and thus affect exact starting dates.

 

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 also organises 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: