Positions for Doctoral Students in computer science (deadline January 27, 2020)

Deadline: Tue, 28.01.2020, this call is closed


Helsinki ICT network: Doctoral student positions in computer science (deadline January 27, 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 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. Helsinki is in the third place in the world’s startup city comparison (Valuer, 2019) 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 January 27, 2020 at midnight Finnish time.

 

Spring call 2020 positions and projects

 

Project 1: Computational methods in personalized medicine

Supervisor: Prof. Sampsa Hautaniemi (Department of Computer Science, University of Helsinki)

The Hautaniemi group focuses on understanding and finding effective means to overcome drug resistance in cancers. Our approach is to use systems biology, i.e., analyze molecular & clinical data from cancer patients with machine learning and mathematical methods, to identify efficient patient-specific therapeutic targets. We coordinate several large personalized medicine consortia, which enables rigorous validation of the predictions from computational analyses as well as translating results to clinic. We have projects available in fields of image analysis, causal analysis, multi-variate statistical analysis and data integration. More information and recent publications: Hautaniemi lab. Please contact Prof. Sampsa Hautaniemi for further information.

 

Project 2: 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 on 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 3: Artificial intelligence for Synthetic Biology

Supervisor: Juho Rousu (Department of Computer Science, Aalto University)

Synthetic biology has a large potential to tackle the issues of circular economy, from converting waste to useful products to carbon-neutral industrial production, through the use of synthetically engineered microbes. These engineering efforts can potentially be expedited with AI-assisted design processes, however, this potential still largely unharnessed. We envision a AI system facilitating the DBTL cycle (Design, Build, Test, Learn), where the strain is designed (D), built in a laboratory (B), measured and tested (T), to learn (L) a model on the current strain to be exploited for the next cycle design phase (D) again. Automatic operation of the DBTL loop has so far been been demonstrated only in selected settings.

In this project, we propose to develop new AI approaches to tackle the problem of accelerating the design of synthetic microbial strains. In short, we propose learning a model to suggest modifications to an existing design by a reinforcement learning approach, where the feedback from the testing of a design is used to propose new, improved designs. The starting point is a reinforcement learning approach called Actor-Critic model, where the Actor component models and updates the policy (what actions to choose in given state) and the Critic component models the value function (goodness of the current state of the system). Such a model has been recently shown to be promising approach for finding genetic modifications that maximise the productivity of synthetic microbial strain. The project will be conducted in collaboration with Synthetic biology team at VTT Technical Research Center of Finland.

The ideal background for the student is an MSc in computer science, mathematics or statistics, with strong experience in machine learning. Experience in reinforcement learning, robotics, control systems engineering, synthetic biology or systems biology is considered as an advantage.   

 

Project 4: Association mining algorithms for biomarker discovery

Supervisor: Dr. 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 5: 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 6: Machine learning and differential privacy

Supervisor: Prof. 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 7: Deep learning for process monitoring and control

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

The goal of this project is to develop accurate predictive models for the performance of real-world industrial systems. The learned models will be used for process monitoring and control. Our approach is a unique combination of the knowledge of the process first principles and the data-driven modeling using deep learning. This is a project within a strategic cooperation of the Aalto university with an industrial partner.

 

Project 8: Deep learning for Cyber Security

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

The goal of this project is to solve interesting data and machine learning problems related to Cyber Security. This is a joint project with F-Secure (https://www.f-secure.com) and you will have a chance to develop deep learning models to analyze a unique data set collected via security software and intelligence systems.

 

Project 9: Deep learning for relational reasoning and planning

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

The interest of our research group is to combine the excellent perception capabilities of deep learning with the ability to reason and plan. Our approach includes models with relational inductive bias and model-based reinforcement learning. Recently relational models such as Transformers and Graph Neural Networks have seen increased popularity, and have given us ability to model relations between objects and entities across wide range of input modalities. Planning using learned world models has shown a lot of promise in increasing the sample complexity of reinforcement learning.

 

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 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 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: Theory of distributed and parallel computing

Supervisors: Prof. Jukka Suomela and Prof. Jara Uitto (Department of Computer Science, Aalto University)

The research groups led by Jukka Suomela and Jara Uitto in the Department of Computer Science at Aalto University are looking for doctoral students to work on the theoretical foundations of distributed and parallel computing.

The goal is to broaden our understanding of models of computing where the communication network is well-connected but the bandwidth is limited, such as the k-machine model, congested clique, BSP, and MPC. Our current research focuses on two main objectives: 1) Computational complexity of statistical inference problems. In particular, we are interested in designing algorithms and proving lower bounds for problems related to sparse matrix multiplication and sparse linear systems. 2) Computational complexity of clustering problems and graph problems. In particular, we aim to design new algorithms for basic primitives such as finding large independent sets and matchings.

The candidates for the positions are expected to have a Master's degree, an excellent knowledge of theoretical computer science and mathematics, and ideally some prior research experience in relevant areas.

 

Project 13: Natural language generation and analysis

Supervisors: Prof. Hannu Toivonen, Dr. Mark Granroth-Wilding (Department of Computer Science, University of Helsinki)

We are looking for a PhD student to join the Discovery Research Group and its two European projects, NewsEye and Embeddia. Both projects revolve around analysis of news stories and generation of new text. NewsEye has a focus on old newspaper archives while Embeddia works with current news. We are now looking for candidates primarily for natural language generation (NLG). The background of an ideal candidate contains both computer science and natural language processing/language technology; previous experience with NLG is not required. Competence in a variety of machine learning techniques is desirable. We offer opportunities for both international and cross-disciplinary collaboration.

 

Project 14: Web Applications and Technologies

Supervisor: Prof. Petri Vuorimaa (Department of Computer Science, Aalto University)

I’m looking for one new doctoral students interested in Web applications and technologies. I’m particularly interested in declarative Web application, user-centered Web development, Web of Things, and machine learning for Web applications. However, you can proposes your own research idea.

 

Project 15: Model-Management Systems

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

The widespread use of machine learning has created a demand for systems that allow analysts to focus on specifying predictive tasks while having limited direct involvement in lower-level decisions for the management of data, models, and computational resources.

This project develops a computational framework and algorithms towards such systems. Requirements: strong analytical and coding skills; background in machine learning, data management, or related field.

 

Project 16: Computer Assisted Bayesian Modeling Workflow

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

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 probabilistic programming. Experience with Stan is preferred but not mandatory.

 

Project 17: New HPC tools to model solar magnetism 

Supervisor: Prof. Maarit Käpylä (Department of Computer Science, Aalto University)

The recently awarded ERC Consolidator Grant project "UniSDyn" has an aim to construct novel computational tools to model the dynamo mechanism in solar-like stars. This is extremely important, as the solar dynamo is responsible for driving space weather and climate, and hence strongly influences our hi-tech society, but constitutes a notoriously difficult problem theoretically and numerically. Capturing the whole Sun in a computer is virtually impossible even with the modern supercomputers, if standard massively parallel computing paradigm with CPUs, using brute force direct numerical simulations, is considered. Therefore, radically new ideas are required! Creating new, more efficient and sustainable, computational tools with accelerator platforms, and using intelligent data analysis methods to characterize and learn the effects that the small-scale dynamics has on the large-scales, to be able to build sub-grid scale models that enable us to create simulation tools in the large-eddy simulation framework, are plausible directions that we want to take in this project. Join our multidisciplinary group consisting of computer scientists, mathematicians, and astrophysicists, and bring in more ideas and expertise! We are currently recruiting both a PhD student and a postdoctoral researcher. To read more about us, please visit: https://www.aalto.fi/en/department-of-computer-science/astroinformatics

 

Project 18: 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/

 

Project 19: Deep reinforcement learning models of human performance

Supervisor: Antti Oulasvirta (possibly with Profs Perttu Hämäläinen, Ville Kyrki, or Samuel Kaski) (Aalto University, Department of Communications and Networking and Department of Computer Science)

This project studies the use of deep learning together with reinforcement learning to model how humans solve complex interactive problems. The project offers an exciting, fully funded possibility for a student interested in the intersection of AI and cognitive science. Prerequisities: deep learning, reinforcement learning

 

Project 20: Software development as learning: The cognitive toolbox of software designers

Supervisor: Dr. Fabian Fagerholm (Department of Computer Science, Aalto University)

Software development is a knowledge-intensive design task that is usually conducted as group work in industrial settings. Software development is part of a larger systems design task where learning about stakeholder needs and requirements play a crucial role. For those creating software designs – which can involve questions from architecture to implementation and operation – the design task is cognitively very demanding. Software designers are known to be prone to cognitive biases (e.g., design fixation), and their performance is influenced by time pressure and cognitive load. Recent research highlights the motivational and emotional processes involved, and reveals that software professionals' decisions are affected when outcomes occur at different points in time.

The aim of this project is to investigate the cognitive toolbox of software designers, i.e., the cognitive processes and strategies software designers use when performing their tasks, and develop interventions for supporting them. The project can take perspectives ranging from educational, where the emphasis is on supporting novice software designers in their learning of cognitive strategies, to industrial, where the emphasis is on creating means for improving the work of professional software designers. The developed interventions may range from methods and approaches to support systems and tools. The applicant is expected to propose a more detailed topic and angle on the project in their application.

Successful applicants are expected to have a background and interest in software engineering, psychology, cognitive science, education, or a closely related field. The applicant is expected to conduct studies in these areas as part of the project. Familiarity with mixed-methods research is considered a plus, as is demonstrations of prior work related to the project area.

The position is fully funded (subject to conditions, please contact the supervisor for more details) and expected to begin in the first half of 2020. The candidate will be supported by international experts in the area of human factors in software engineering.

 

Project 21: Edge and Fog Computing

Supervisor: Prof. Mario Di Francesco (Department of Computer Science, Aalto University)

We are looking for a doctoral student within a research project on Edge / Fog Computing for the Internet of Things (IoT) funded by the Academy of Finland, the leading Finnish funding agency. The project aims to devise novel mathematical and software tools to optimize the performance of large-scale IoT applications over heterogeneous devices, with a focus on reliable and low-latency operations. The project is a collaboration between Aalto University, the University of Oulu, VTT Technical Research Center of Finland, and several research groups worldwide. Thus, it provides an excellent chance for internationalization, thereby enabling wide opportunities for career development in both industry and academia.

 

Project 22: Open doctoral student position in Prof. Janne Lindqvist’s group – security engineering, usable security and human-computer interaction

Supervisor: Prof. Janne Lindqvist (Department of Computer Science, Aalto University)

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 https://www.lindqvistlab.org/.



Project 23: 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)

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 https://www.lindqvistlab.org/.

 

Project 24: Bayesian deep learning with application to design bacterial genomes

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

Synthetic biology aims to manipulate and optimize bacterial species that are used in industrial and sustainable biotechnology. Advanced computational and bioinformatics methods have a central role in analysing large data sets from synthetic biology experiments and in designing bacterial proteins/genomes to achieve desired bioengineering goals. We are looking for a doctoral student to develop Bayesian deep generative models to analyse large-scale genetic data sets from high-throughput screening experiments and to optimize bacterial genomes to optimize protein expression in selected species relevant for industrial applications. Thesis work would include both development of Bayesian deep learning methods and applications to exciting real-world data from our collaborators in Europe. Applicants are expected to have good knowledge of machine learning/statistics, programming, and interest in developing/applying probabilistic methods for bioinformatics and synthetic biology. For more information and relevant recent work, see (http://research.cs.aalto.fi/csb/publications) or contact Harri Lähdesmäki (harri.lahdesmaki@aalto.fi).



Project 25: Bayesian deep learning

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

Bayesian inference methods for deep learning models promises to provide robust learning that are not sensitive to overfitting and provide reliable uncertainty estimates. Our research group's recent work include recently proposed deep/nonparametric differential equation models that make it possible to learn arbitrary continuous-time dynamics from data without any prior knowledge. These models can also be used to implement state-of-the-art deep learning methods in the context of deep Gaussian processes or neural networks. These high-capacity continuous-time models can, however, suffer from over-fitting. We are searching for a doctoral student to work on the fascinating topic of Bayesian deep learning. Thesis work would involve further developing deep learning models and implementing Bayesian inference methods (MCMC, variational inference) for robust inference. The work would include e.g. non-parametric probabilistic modelling, deep continuous-time models, deep Gaussian processes, and/or deep generative models (based on your preference). Applicants are expected to have good knowledge of machine learning, mathematics, statistics, and programming. For more information and relevant recent work, see (http://research.cs.aalto.fi/csb/publications) or contact any of the academic contact persons listed above.

 

Project 26: 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) simulator-based inference: how to combine first-principles models with learning from data, (2) Bayesian deep learning, (3) reinforcement learning and inverse reinforcement learning, and their combinations. We have a few other exciting topics as well; contact me for more information!
Links: https://research.cs.aalto.fi/pml/research.shtml

 

Project 27: Probabilistic interactive user models for interactive AI

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 specific goals and plans, the system can only help them reach those goals if it understands them. Since the goals can be tacit and changing, they need to be inferred from observations and interaction. We develop the probabilistic interactive user models and inference techniques needed to understand other agents and how to assist them more efficiently. If I was choosing a thesis topic right now, this would be the one. Additional keywords: active learning, experimental design, knowledge elicitation, multi-agent learning, machine teaching, reinforcement learning

Links: https://research.cs.aalto.fi/pml/ , https://aaltopml.github.io/machine-teaching-of-active-sequential-learners/


Project 28: 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 29: Optimizing Edge-Cloud Systems for Big Data and Machine Learning Applications

Supervisor: Prof. Hong-Linh Truong, Aalto Systems and Services Engineering Analytics (http://rdsea.github.io) (Department of Computer Science, Aalto University)

The topic will focus on researching optimization techniques for edge-cloud software systems empowering big data and machine learning applications. Such techniques aim at assuring reliability and robustness of complex Big Data and ML applications and services running in continuum edge-cloud environments. The work will develop techniques for performance engineering, incident monitoring and analytics, configuration management, and testing and debugging of Big Data and ML applications. The topic is expected to carry out experiments/prototypes with Big Data and Machine Learning applications developed with state-of-the art dataflows/programming frameworks and with edge-cloud continuum systems considering  new emerging hardware architectures for AI.

 

Project 30. Data mining tools for dynamic graphs

Supervisor: Prof. Nikolaj Tatti (Department of Computer Science, University of Helsinki)

We are searching for PhD students to work on the topic of designing new algorithms for analysing large graphs, especially with timestamps. The goal is to find interesting insights from large graphs that otherwise would go unnoticed. A typical approach entails designing and formulating an optimisation problem, analysing the problem, designing, analysing, and implementing a solver to a problem, and conducting case studies on real-world data. Applications for such tools include, but not limited to, network traffic analysis, social media network analysis, specifically detecting malicious behaviour or fake news.

Experience in programming, algorithm design, computational complexity, graph theory, combinatorics, machine learning, and statistics is a plus.

 

Application process

The Spring 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 January 27, 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 February 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.
  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 Christmas 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.

 

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: