31 Funded PhD Programs in Computer Science at Helsinki ICT, Finland

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Helsinki ICT, Finland invites online application for multiple fully funded PhD Programs in Computer Science. Candidates interested in fully funded PhD positions can check the details and may apply as soon as possible. The online application form opens on December 12, 2022 and closes on January 29, 2023 11:59pm Finnish time.

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 80 professors and over 200 doctoral students, and the participating units graduate altogether more than 40 new doctors each year.

The participating units of HICT have currently funding available for qualified doctoral students. We offer an exciting opportunity to join world-class research groups, with 30 research projects to choose from. The activities of HICT and the themes of open positions 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

If you wish to be considered as a new doctoral student in HICT you can apply to one or a number of doctoral student positions. We actively work to ensure our community’s diversity and inclusiveness. This is why we warmly encourage qualified candidates from all backgrounds to join our community.

1. PhD Programs in Explainable and robust AI for scientists 

Summary of PhD Positions:

Machine learning and AI are extensively used in the sciences. When modelling physical systems, the understandability and statistical robustness of the models is often more important than predictive accuracy. We are looking for talented postdoctoral researchers and doctoral students to study explainable and understandable AI and the uncertainty quantification of AI models. While the AI methods we develop are generic and not tied to any specific application domain, we work closely with scientists to build Virtual Laboratory for Molecular Level Atmospheric Transformations.

2. PhD Programs in Green NLP – controlling the carbon footprint in sustainable language technology

Summary of PhD Positions:

GreenNLP addresses the problem of increasing energy consumption caused by modern solutions in natural language processing (NLP). Neural language models and machine translation require heavy computations to train and their size is constantly growing, which makes them expensive to deploy and run. In our project we will reduce the training costs and model sizes by clever optimizations of the underlying machine learning algorithms with techniques that make use of knowledge transfer and compression.


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3. PhD Programs in Bayesian Workflows for Iterative Model Building and Networks of Models

Summary of PhD Positions:

Statistical analysis is critical when it comes to obtaining insights from data. Despite the practical success of iterative Bayesian statistical model building, it has been criticized to violate pure Bayesian theory and that we may end up with a different model had the data come out differently. In this project, we formalize and develop theory and diagnostics for iterative Bayesian model building. The practical workflow recommendations and diagnostics guide the modeller through the appropriate steps to ensure safe iterative model building, or indicate when the modeler is likely to be in the danger zone.


4. PhD Programs in Trust-M: Designing Inclusive & Trustworthy Digital Public Services for Migrants in Finland


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5. PhD Programs in Neuro-symbolic multiagent reinforcement learning

Summary of PhD Positions:

Researchers interested in postdoctoral/doctoral positions are invited to apply for this project. Here, we shall study a new approach to synthesis of efficient communication schemes – including learning of novel concepts – in cooperative multi-agent systems, trained via reinforcement learning. We combine symbolic methods with machine learning, in what is referred to as a neuro-symbolic system, where a neural network learns to produce programs in a symbolic language to solve a task at hand.


6. PhD Programs in Machine learning for wireless and mobile systems

Summary of PhD Positions:

Machine learning (ML) is the foundation of artificial intelligence in today’s applications. The scope of ML is wide, including (but not limited to) speech synthesis and recognition, machine translation, and computer vision. This project involves designing and (or) applying ML techniques to the scenarios represented by wireless and mobile systems.

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7. PhD Programs in Cloud-native systems: software systems and security

Summary of PhD Positions:

Cloud computing has introduced a new model for provisioning resources and services over the Internet on top of a virtualized, elastic infrastructure. Additional paradigms have also recently emerged: edge, fog, and serverless. Moreover, modern (namely, cloud-native) applications are generally composed of multiple microservices, realized as software containers and managed through an orchestrator such as Kubernetes. This project involves addressing different aspects of cloud-native systems, with focus on software systems and security.


8. PhD Programs in Transformer methods for hyperspectral image fusion

Summary of PhD Positions:

The project will study and develop new Transformer-based models for modeling of multispectral, hyperspectral and lidar based remote sensing data in varying resolutions. We will analyze the information content of modern and future hyperspectral earth observation and lidar data obtained over the boreal forest zone. The results will be used for studying the biodiversity of forests based on the distrubution of tree species and the spatial arrangements of individual trees. In addition, the models will be included in a digital twin of the Finnish forests. The project aims at novel methodological and algorithmic improvements by using state-of-the-art Transformer models that have already been studied and developed in the research group.


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9. PhD Programs in Transformative AI Collaborations

Summary of PhD Positions:

Aalto University has launched a new collaborative initiative in transformative research to support cooperation between AI and Data Science researchers with researchers in other fields. The aim is to create new knowledge by developing AI methods to advance research in other domains. We are especially seeking candidates interested in collaborations between Artificial Intelligence and/or Data Science for Transformative AI collaborations.

10. PhD Programs in Machine learning for drug design

Summary of PhD Positions:

Recent progress in machine learning for generative and predictive models of molecules brings us towards computational, automatised drug design. We develop statistical methods and models for molecular structures, energies and interactions with the help of deep learning. A number of open problems reside in developing neural network models with physics-based inductive biases, in generative models in 3D spaces, in modelling the property landscapes of molecules, and in generalizing outside the training distribution in molecular design.

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11. PhD Programs in Probabilistic modelling and Bayesian inference for machine learning

Summary of PhD Positions:

I am looking for a new doctoral student in my team which develops probabilistic modelling and Bayesian inference methods. The team has several exciting new machine learning formulations we work on, and opportunities for applying the methods with top-notch collaborators. But the core is always development of new methods, and with this call I am looking for talented researchers with background in machine learning, stats or CS (or other directly relevant topics) who are keen on developing the new methods. In the cover letter, let me know what you are interested in – if we are already working on it, all the better, but I am willing to listen to new ideas too.

12. PhD Programs in Multi-agent modeling for human-AI interaction

Summary of PhD Positions:

We develop AI assistants which help people make better decisions, with ongoing applications in science and engineering. We use multi-agent formalisms to define the assistance problems these assistants solve, including the human being assisted, and employ models of human behavior to (pre-)train them in silico. We build models of human behavior using (multi-agent) reinforcement learning, based on theories of human behavior from cognitive science. In this project you will develop novel multi-agent formalizations of assistance and create new models of human behavior for these formalizations. The focus will be on maximally autonomous assistants — assistants that automate a person’s task as much as possible, while using a minimal interaction to learn to solve said person’s task well.


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13. PhD Programs in Maximally autonomous AI

Summary of PhD Positions:

In this project, you will develop principles and methods for AI assistants to be maximally autonomous. The goal is to automate to the greatest extent possible, while using a minimal interaction with the person being assisted. Interaction is important to learn the person’s goal, but should be done sparingly. It should only happen when necessary, i.e. when it can help reduce uncertainty about the goal and improve the assistant’s long-term decision making.


14. PhD Programs in Deep learning with differential privacy

Summary of PhD Positions:

Differential privacy allows developing machine learning algorithms with strong privacy guarantees. Recent work shows it is possible to combine strong privacy and high accuracy by pre-training models on public data and only fine-tuning the model with the sensitive data. However, high accuracy still requires care for example in hyperparameter tuning. The aim of this project is to develop methods that make it easier to train high accuracy private models. The project will benefit from a very large grant of compute time on LUMI, 3rd fastest supercomputer in the world. The project requires a background in deep learning.

15. PhD Programs in Learning-based control for neural speech synthesis

Summary of PhD Positions:

This project aims to develop an interactive and intuitive neural speech synthesizer that encompasses both explicit control using signal processing and latent control using learning-based methods. Easy-to-use control beyond text will enable users, such as voice artists, creators, and linguists to interactively modify and adjust their voice synthesizer output. The ideal candidate for this doctoral research project has a strong background in machine learning and a working knowledge of deep learning Python libraries (such as PyTorch). Further background in speech and/or audio signal processing, C++ DSP programming, and a general interest in sound synthesis is beneficial.


16. PhD Programs in Machine Learning for Health (ML4H)

Summary of PhD Positions:

Accumulation of massive amounts of health data has enabled researchers to address questions such as: how to accurately predict the risk of disease, how to personalize treatments based on real-time data from wearable devices, or how to use genomic data to understand disease or antibiotic resistance. Central challenges in ML4H include noisy data, multiple heterogeneous data sources including images and text, learning about causality, interpreting the models, and quantifying the uncertainty, to name a few. We tackle these by developing models and algorithms which leverage modern machine learning principles.


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17. PhD Programs in Amortized inference for experimental design and decision making

Summary of PhD Positions:

We develop amortized experimental design and inference techniques that take into account the down-the-line decision making task. For example, this may include delayed-reward decision making where data has to be measured, at a cost, before making the decision. This problem occurs in the design-build-test-learn cycles which are ubiquitous in engineering system design, and experimental design in sciences and medicine. The solutions need Bayesian experimental design techniques able to work well with simulators, measurement data and humans in the loop, who are both information sources and the final decision makers.


18. PhD Programs in Amortized surrogates for simulation and inference

Summary of PhD Positions:

Recent advances in machine learning have shown how powerful emulators and surrogate models can be trained to drastically reduce the costs of simulation, optimization and Bayesian inference, with many trailblazing applications in the sciences. In this project, the candidate will join an active area of research within several FCAI groups to develop new methods for simulation, optimization and inference that combine state-of-the-art deep learning and surrogate-based kernel approaches – including for example deep sets and transformers; normalizing flows; Gaussian and neural processes – with the goal of achieving maximal sample-efficiency (in terms of number of required model evaluations or simulations) and wall-clock speed at runtime (via amortization).


19. PhD Programs in Deep learning for material science

Summary of PhD Positions:

The goal of the project is to develop novel deep learning algorithms to answer open questions in nanoscale physics such as predicting molecular structure in liquids or developing accurate, but very fast models of water. In particular we want to take advantage of the capabilities of Graph Neural Networks to provide robust and highly efficient simulators for molecular systems. These models will be coupled to state-of-the-art experimental characterisation, ultimately including a dynamic interaction where simulations are actively used to focus on information rich regions during experiments. We are looking for applicants with a strong background in deep learning and/or physical simulations.

20. PhD Programs in Deployment as a fundamental ML challenge

Summary of PhD Positions:

Machine learning (ML) is now used widely in sciences and engineering, in prediction, emulation, and optimization tasks. Contrary to what we would like to think, it does not work well in practice. Why? Because the conditions during deployment may radically differ from training conditions. This has been conceptualized as distribution shift or sim-to-real gap, and a particularly interesting challenge which we will be tackling is changes due to unobserved confounders. Solving this challenge is imperative for widespread deployment of ML. In this project, we will consider deployment as a fundamental machine learning challenge, developing new principles and methods for tackling this problem which can be argued to be the main show-stopper in making machine learning seriously useful in solving the real problems we are facing, in sciences, companies and society.


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21. PhD Programs in Efficient probabilistic modeling of speech

Summary of PhD Positions:

Probabilistic models are essential for capturing the stochastic properties in speech and audio signals. Speech synthesis in particular has recently emerged as a proving ground application for deep generative models. Autoregressive (AR) density models, such as WaveNet, achieve high quality, can be trained effectively using maximum likelihood, and have low algorithmic latency suitable for real-time applications. However, available implementations of AR inference using GPUs and popular Python libraries are slow, which has shifted research to feedforward models for parallel inference.

22. PhD Programs in Evaluating and improving posterior inference for difficult posteriors

Summary of PhD Positions:

Both MCMC and distributional approximation algorithms (variational and Laplace approximations) often struggle to handle complex posteriors, but we lack good tools for understanding how and why. We study diagnostics for identifying the specific nature of the computational difficulty, seeking to identify e.g. whether the difficulty is caused by narrow funnels or strong curvature. We also develop improved inference algorithms that account for these challenges, e.g. via automated and semi-automated transformations for making the posterior easier or by better accounting for the underlying geometry. We are looking for applicants with a strong inference background and interest in working on improving inference for the hardest problems.


23. PhD Programs in Explainable AI for virtual laboratories

Summary of PhD Positions:

FCAI is actively developing methods and software for virtual laboratories to enable AI assistance in the research process. We are looking for a candidate to research explainable AI and uncertainty quantification. Efficient human-AI collaboration requires methods that are either inherently capable of providing explanations for the decisions or can explain the decisions of other AI models. For instance, the user needs to know why AI recommends a particular experiment or predicts a specific outcome. They should always be aware of the reliability of the AI models. You will conduct the project with a team of AI researchers with access to researchers specialised in various application areas. The applicant should be interested in incorporating the techniques as part of virtual laboratory software developed at FCAI for broad applicability.


24. PhD Programs in Foundation models for interactive computing

Summary of PhD Positions:

Foundation models such as GPT-3 and CLIP are revolutionizing how AI is developed and applied, by providing reusable and general-purpose building blocks with unprecedented capabilities. Instead of training large-scale models from scratch for thousands or millions of GPU hours, one can solve novel tasks by combining pretrained foundation models in novel ways, or finetuning them for downstream tasks. The release of OpenAI’s CLIP, for instance, soon led to the emergence of CLIP-guided image generation models such as Disco Diffusion, Stable Diffusion, Midjourney, and DALL-E 2, as well as smaller-scale experiments such as ClipDraw and StyleClipDraw. CLIP also increasingly empowers various semantic search solutions across multiple industries.

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25. PhD Programs in Language-empowered world models for RL

Summary of PhD Positions:

Incorporating accurate causal knowledge about an environment in terms of objects and their relationships into the world model of a reinforcement learning agent can yield significant improvements in reducing the amount of exploration required to solve new tasks and to generalize to new environments. Recently, causal representation learning has been proposed as a way of extracting and representing such causal knowledge from previous experience in the latent space. However, in practice data are extremely correlated and it is not straightforward to come up with ways to even disentangle relevant objects, not to mention the causal relationships between them.


26. PhD Programs in Long term planning with search graphs

Summary of PhD Positions:

In this project, we investigate novel extensions of MCTS for continuous domains based on search graphs. Our approach is built on the idea that sharing the same action policy between several states can yield efficient planning and thus high performance. This results in a limited number of stochastic action bandit nodes to produce a layered graph instead of an MCTS search tree allowing for long term planning. The designed algorithms can be used for robotic manipulation and navigation, for example, with a Boston Dynamics Spot robot. We are looking for applicants with a strong background in reinforcement learning (especially model-based) and tree search algorithms.


27. PhD Programs in ML4Science

Summary of PhD Positions:

Machine learning is increasingly being used as a key element in research, for instance to efficiently approximate computationally costly simulations, automate design of experiments, and for integrated analysis of experimental results and multi-fidelity simulations. Much of the practical work is done in the context of specific applications in science, but our interest lies in the more general question of how ML could be used as part of the research process, essentially to improve the results and the scientific process itself. We seek solutions that work across multiple disciplines and applications.


28. PhD Programs in Multi-agent RL for collaborative AI

Summary of PhD Positions:

We develop the new ML principles and methods needed by AI assistants to help people make better decisions, with ongoing applications in science and engineering. We use multi-agent formalisms to define the assistance problems these assistants solve, including the human agent being assisted, and develop new multi-agent RL solutions for the problem. We are particularly interested in (1) how to build and (pre-)train models of human behaviour based on cognitive science, and (2) how to solve new ad-hoc teamwork problems with multi-agent RL.


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29. PhD Programs in Private federated learning

Summary of PhD Positions:

Many applications of machine learning require training on distributed data while keeping the data private. Private federated learning enables this, but its communication requirements can be impractical. The aim of this project is to develop new approaches and methods for private learning on distributed data. A strong background in differential privacy and/or federated learning is an asset for this project.


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30. PhD Programs in Sustainability in computing

Summary of PhD Positions:

Sustainability is important in the context of AI and in particular there exist AI/ML/DS methods to improve sustainability of a given system using say AI methods. However, the computational methods used in AI are not always sustainable as they might require a lot of data or a lot of computational power produce the results. The aim of this research project is to take a look at sustainable AI methods and in particular sustainable computational methods whose energy fingerprint is minimal. One possible approach that has recently been studied is the clever use of parallel and distributed algorithms to decrease the amount of energy per flop. We are looking for applicants with interests in energy efficient or otherwise sustainable computational methods in general.


31. PhD Programs in Workflows for better priors

Summary of PhD Positions:

Bayesian models rely on prior distributions that encode knowledge about the problem, but specifying good priors is often difficult in practice. We are working on multiple fronts on making it easier, with contributions to e.g. prior elicitation, prior diagnostics, prior checking, and specification of priors in predictive spaces. We welcome applicants looking to work on any of these aspects and contribute to both theoretical development and practical software for aiding the prior specification process.

How to Apply?

Read through the list of available positions and select the topic(s) that you are interested in. Click on the link of eRecruitment system to apply to the topic that you are interested in. You can apply directly to one or multiple research projects. All the supervisors you indicate on your application form will be informed of your interest, and others also have access to your application documents.

Eligibility Criteria

The HICT calls are targeted to 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. Current funded 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.

If you become selected as a new doctoral student, you need to apply for a study right for doctoral studies either at Aalto University or the University of Helsinki. Your new supervisor will assist you with the application. HICT itself does not award doctoral study rights or doctoral degrees. All doctoral students within the HICT network are doctoral students of either Aalto University or the University of Helsinki.

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Compulsory Attachments

All materials should be submitted in English in a PDF format. Note: files should be 5MB max. You can upload multiple files to the eRecruitment system, each 5MB.

1. Letter of motivation (max. tw0 pages). 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. In the Finnish university system, a person must have a Master’s degree in order to enroll for doctoral studies. 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 from 2 senior academic people. We will contact your referees, if recommendation letters are required.

Application Deadlines: January 29, 2023 11:59pm Finnish time

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