We offer the possibility to join world-class research groups either at Aalto University or University of Helsinki, with multiple interesting research projects to choose from. We organise two open calls for new doctoral students annually.
Our fully-funded doctoral student positions have gained a lot of international interest since their introduction in 2014. We appreciate applicants with diverse backgrounds and enthusiasm in computer science.
The quality of research and education in both HICT universities is excellent and our doctoral students are typically hired as full-time employees for the duration of their doctoral studies. The average duration of doctoral studies is four years in Finland.
You are most welcome to apply for HICT doctoral student positions! Our open positions are announced on this webpage.
Summer call 2021 – How to apply?
- Get familiar with the application process, eligibility requirements and compulsory attachments described on General HICT Call FAQ.
- Choose the projects from the list below.
- Fill in the application form and send it before deadline.
The call is closed. Thank you for your application!
The application form closes on August 16, 2021 at midnight Finnish time (23:59 EET, Eastern European Time).
Projects in the HICT Summer call 2021
- Project 1. Doctoral student positions in Prof. Janne Lindqvist’s group
- Project 2. Doctoral student positions in HAIC (Helsinki-Aalto Institute for Cybersecurity)
- Project 3: Virtual Atmospheric Laboratory
- Project 4: Bayesian workflow for iterative model building
- Project 5: Eco-evolutionary control theory and its applications to drug therapies
- Project 6: Machine Learning for Improved Combinatorial Cancer Therapies
- Project 7: Artificial intelligence for Synthetic Biology
- Project 8. Open doctoral student position in Keijo Heljanko’s group – Massively parallel distributing computing with GPUs
- Project 9: AI Methods for Fusion Energy Research
- Project 10. Learning with probabilistic principles
- Project 11: Large-scale and hybrid computing on modern architectures and systems: Programming models, Designs and Optimization
- Project 12: Deep generative modeling for precision medicine and future clinical trials
- Project 13: Deep learning for continuous-time differential equation systems
- Project 14: Physics-inspired geometric deep representation learning for drug design
- Project 15: Probabilistic modelling for collaborative human-in-the-loop design
- Project 16: Machine Learning for Health (ML4H)
- Project 17: Machine learning and differential privacy
- Project 18: Methods for large scale fault-tolerant quantum computing
- Project 19: Data science for mental health and well-being
Choose your topics below and fill in the application form.
Project 1. Doctoral student positions in Prof. Janne Lindqvist’s group
Please contact Janne Lindqvist at the Aalto email address about these positions during August 2021.
This topic includes three open positions:
Project: Science of human-computer interaction
Are you frustrated in the lack of science in the field of human-computer interaction (HCI)? We are looking for doctoral students interested in transforming science in HCI. 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 website for the group: https://www.lindqvistlab.org/.
Project: Multitasking and productivity tools
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 website for the group: https://www.lindqvistlab.org/.
Project: Mixed methods HCI and security research
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 website for the group: https://www.lindqvistlab.org/.
Project 2. Doctoral student positions in HAIC (Helsinki-Aalto Institute for Cybersecurity)
Please contact Janne Lindqvist at the Aalto email address about these positions during August 2021.
This topic includes two open positions:
Project: HAIC: security engineering and usable security
We are looking for doctoral 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 the website for the group: https://www.lindqvistlab.org/. The students will also get to participate in the activities of HAIC.
Project: HAIC: artificial intelligence and machine learning for human-computer interaction
We are looking for doctoral students interested in developing novel artificial intelligence and machine learning approaches to human-computer interaction.. 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.
Project 3. Virtual Atmospheric Laboratory
Supervisor(s): Prof. Kai Puolamäki, (Department of Computer Science, University of Helsinki), Prof. Hanna Vehkamäki, Dr. Theo Kurtén
We are looking for one or more students to join us to build Virtual Atmospheric Laboratory (VATLAB). The purpose of the VATLAB is to help us efficiently model atmospheric processes and to understand the underlying processes and causal connections. VATLAB will combine first-principles quantum chemical and other simulations, probabilistic machine learning (ML) models, and interactive visualizations. In this project, the hired student will work together with atmospheric scientists on the specific problem of atmospheric cluster formation, but the developed computational methods will be applicable in other domains too. The student’s task involves research in probabilistic modelling and interaction methods to explore the data and build the ML models.
The applicant will work in a multidisciplinary team involving both computer scientists and atmospheric scientists, with the thesis work containing publications in both domains. A successful applicant should have basic knowledge of ML and related mathematics. Knowledge of and interest in natural sciences is considered an advantage, but specific prior knowledge of atmospheric clustering processes is not required. We will consider students with background related to fields in computer or atmospheric sciences. We will also consider outstanding MSc students who wish to continue with PhD studies after graduation. The details of the project and supervision arrangements can be tailored depending on qualifications and preferences of the applicant.
The project will be done in collaboration with the Department of Computer Science and Institute for Atmospheric and Earth System Research at the University of Helsinki. The project is part of the Academy of Finland funded Finnish Center for Artificial Intelligence (FCAI) and Atmosphere and Climate Competence Center (ACCC) flagships.
Project 4: Bayesian workflow for iterative model building
Supervisor: Prof. Aki Vehtari (Department of Computer Science, Aalto University)
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. We show that when the iterative model building is done carefully, the difference to the theoretically optimal result is negligible.
The practical diagnostics guide the modeller through the appropriate steps to ensure safe iterative model building, or indicate when the modeller is likely to be in the danger zone. By making applied scientific research and data analysis more reliable and reproducible, our understanding of the world and decision-making will be improved.
Project 5: Eco-evolutionary control theory and its applications to drug therapies
Supervision: Prof. Ville Mustonen (University of Helsinki); This is a collaborative project with the Lässig Group at the University of Cologne.
Predictability and control of evolving populations is an emerging topic of high scientific interest and vast translational potential in applications such as vaccine and therapy design. This project will extend eco-evolutionary control theory to model free and multi-species contexts. For example, we will develop methods to find optimal control protocols for cell populations, find out what are the key determinants of controllability of a multi-species community, and apply the results to therapy optimisation. These projects are at the interface of microbial evolution, statistical physics, and information theory. A highly quantitative background and a burning interest in evolution are required.
Keywords: stochastic optimal control, eco-evolutionary dynamics, evolutionary theory, drug resistance.
 Lässig M, Mustonen V, Walczak AM (2017) Predicting evolution. Nat Ecol Evol 1(3):1–9.
 Lässig M, Mustonen V, (2020) Eco-evolutionary control of pathogens. PNAS, 117 (33), 19694-19704
Project 6: Machine Learning for Improved Combinatorial Cancer Therapies
Supervisors: Prof. Juho Rousu (Department of Computer Science, Aalto University), Tero Aittokallio (FIMM), Antti Airola (University of Turku), Tapio Pahikkala (University of Turku)
Several positions are open in a project with the aim to develop new tools for finding better treatments for complex diseases such a cancer, by finding combinations of drugs that work better than the drugs in isolation, both in terms of the efficacy of the combination therapy in treating the disease and the side-effects. The project builds on a recent breakthrough of predicting drug combination responses using machine learning with very high accuracy (Julkunen et al., Nature Communications, 2020). The project will further develop and and apply advanced machine learning and optimisation tools to achieve the results. The positions are affiliated to a large multi-year research grant “Machine Learning in Systems Pharmacology” funded by Academy of Finland for 2021-2025.
Reference: Julkunen, H., Cichonska, A., Gautam, P., Szedmak, S., Douat, J., Pahikkala, T., Aittokallio, T. and Rousu, J., 2020. Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects. Nature communications, 11(1), pp.1-11.
Project 7: Artificial intelligence for Synthetic Biology
Several full-time PhD positions are open in developing Artificial intelligence methods and tools for synthetic biology, using state-of the art machine learning, deep learning and reinforcement learning techniques. Applications in synthetic biology include enzyme design, pathway retrosynthesis, and strain design and optimisation. The positions belong to the Centre for Young Synbio Scientists (cyss.fi) and funded by Jenny and Antti Wihuri Foundation.
Project 8. Open doctoral student position in Keijo Heljanko’s group – Massively parallel distributing computing with GPUs
The PhD Student position is in the context of the Academy of Finland project “Design and Verification Methods for Massively Parallel Distributed Systems (DeVeMaPa)”. The main focus of the project is on the use of GPU computing to accelerate Big Data processing and the theoretical foundations of these systems. Use cases include Bioinformatics applications and Big Data analytics.
The project will develop methodology for the design and verification of massively parallel heterogeneous computing. We need new methods to support the massive increase in the amount of parallelism at all levels of the hardware/software stack. Such massive increases in parallelism will make some currently used programming paradigms infeasible and thus new methods need to be devised to cope with industrial Big Data use cases. These methods must also be accompanied with solid theoretical foundations, allowing for the development of automated testing and verification tools that are required to validate the parallelization runtime software before production deployment. A key challenge is the need for seamless integration of heterogeneous computing with GPUs and hardware accelerators (e.g., neural network accelerators), how can they all be handled in a unified Big Data programming framework?
Project 9: AI Methods for Fusion Energy Research
Supervisor: Prof. Jukka K. Nurminen (Department of Computer Science, University of Helsinki)
This project is related to the utilization and development of AI methods for fusion research. The project is part of the newly started Advanced Computing Hub located at the University of Helsinki (see e.g. https://www.csc.fi/en/-/three-million-euros-in-eu-funding-for-finnish-research-into-fusion-energy-and-artificial-intelligence). The Hub will assist the European fusion research community and provide computing related expertise with special emphasis on the application of AI and data science methods.
We are looking for a PhD student with prior knowledge of data science and interest to apply AI methods to computing intensive physics problems. Tasks to tackle include, but are not limited to, dimension reduction, surrogate models, and application of data science methods to simulation data.
The Helsinki hub will have close to ten experts with different backgrounds. For this particular position basic knowledge of physics (especially flow modelling) is an advantage but not mandatory.
Keywords: Applied data science, AI, ML, Reduced models, Fusion
Project 10. 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 methods with deep learning, real-time inference, and dynamical models. This project relates to various topics in this space: Bayesian deep learning, computer vision, generative models, stochastic differential equations, meta-learning, Gaussian and neural processes, and normalizing flows.
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. Successful candidates are expected to have completed a Masters’s degree and have familiarity with machine learning and statistics.
For more information and recent publications and pre-prints, see the supervisor’s home page at http://arno.solin.fi
Project 11: Large-scale and hybrid computing on modern architectures and systems: Programming models, Designs and Optimization
The topic will focus on researching and developing techniques and tools for large-scale and hybrid computing on modern architectures and systems. We will investigate emerging programming models, such as function-as-a-service, hybrid workflows of traditional data analysis pipelines and machine learning pipelines, and human-in-the-loop in large-scale, complex analysis. Novel software techniques and methods will be developed for support the developer to design, manage and optimize large-scale computing and data analysis applications. Observability, performance, elasticity, fairness and interpretability will be particular features in the focus of the design and optimization.
Large-scale and hybrid computing will be targeted to emerging solutions for large-scale and data-intensive applications, which combine traditional workflow-based large-scale data analysis with machine learning, whereas modern systems will be high-performance computing systems with CPU/GPU, large-scale containerized systems, cloud/edge systems with AI accelerators, and potentially quantum computing systems.
- Very good knowledge of large-scale computing systems, and cloud and edge computing system
- Very good in programming, especially with Python, C, GoLang, Java
- Familiar with workflows and ML pipelines
- Very good knowledge in tools for programs and systems monitoring and analysis (e.g., instrumentation, tracing, system monitoring)
Project 12: Deep generative modeling for precision medicine and future clinical trials
Supervisor: Prof. Harri Lähdesmäki (Department of Computer Science, Aalto University)
We are looking for a PhD student to develop probabilistic machine learning methods for heterogeneous health datasets from large-scale biobanks, clinical trials and single-cell experiments. This project develops novel deep generative modeling methods to (i) predict adverse drug effects using longitudinal/time-series data from large-scale clinical trials, and to (ii) harmonize large-scale health data sets for AI-assisted decision making to revolutionize future clinical trials.
Methodologically this project includes e.g. VAEs, GANs, Bayesian NNs, domain adaptation, and Gaussian processes. The work will be done in collaboration with research groups from the Finnish Center for Artificial Intelligence, and the novel methods will be tested using unique real-world data from our collaborators in university hospitals and big pharma company.
See our recent work:
Project 13: Deep learning for continuous-time differential equation systems
Supervisor: Prof. Harri Lähdesmäki (Department of Computer Science, Aalto University)
Recent machine learning breakthroughs include black-box modeling methods for differential equations, such as Gaussian process ODEs  and neural ODEs. These methods can be viewed as “infinitely” deep learning methods where the traditional neural nets (implemented with a finite number of layers) are replaced with continuous-time differential equation system which, in turn, are parameterized by deep NNs, thus implementing deep learning methods that are effectively “infinitely” deep. These methods can be applied to standard classification and regression tasks but they are particularly useful to learn arbitrary continuous-time dynamics directly from data.
We are looking for a PhD student to join our current efforts in (i) developing efficient Bayesian methods for robust learning of infinitely deep models from data, (ii) developing neural ODEs to learn unknown/arbitrary dynamics of high-dimensional systems (e.g. in robotics, biology, physics or video applications) using a low-dimensional latent space representation, and (iii) further developing these methods for reinforcement learning.
See our recent work:
Project 14: Physics-inspired geometric deep representation learning for drug design
We invite applications for a doctoral student position in geometric deep learning aimed at advancing state of the art in drug design. Key research directions include new 3D generative models for molecules that reflect their underlying physical-chemical processes and dynamics, spatial constraints, local invariances and equivariances, and energy considerations; domain generalization in both structural and sequence spaces; deep geometric models for inducing diversity in molecular generation; learning with limited training data; and non-autoregressive inference methods.
The work will augment and consolidate existing well-developed research pipelines of the supervisors. The selected student may also have the opportunity to work with and visit our collaborators at leading pharma companies and academic groups in Europe, USA, and Canada. Facility in implementing deep learning models is expected, and training in one or more of the following would be a plus: Statistical Mechanics, Bayesian Learning, Graph Neural Networks, Generative Models, Ordinary and Partial Differential Equations, and Computational Biochemistry. Some representative publications are given below.
 John Ingraham, Vikas Garg, Regina Barzilay, Tommi Jaakkola. Generative Models for Graph-Based Protein Design. NeurIPS (2019).
 Vikas Garg, Stefanie Jegelka, Tommi Jaakkola. Generalization and Representational Limits of Graph Neural Networks. ICML (2020).
 Cagatay Yildiz, Markus Heinonen, Harri Lähdesmäki. ODE2VAE: Deep generative second order ODEs with Bayesian neural networks. NeurIPS (2019)
 Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski. Deep learning with differential Gaussian process flows. AISTATS (2019), notable paper award (top 1%)  Kyle Barlow, Shane O Conchuir, Samuel Thompson, Pooja Suresh, James Lucas, Markus Heinonen, Tanja Kortemme. Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein-Protein Binding Affinity Upon Mutation. Journal of Physical Chemistry B, 122(21):5389-5399 (2018)
Project 15: Probabilistic modelling for collaborative human-in-the-loop design
We are looking for a doctoral student interested in developing probabilistic modelling and inference methods needed for complex design tasks, with drug design as a case study. The idea is to help experts steer the modelling system towards their design goals, while eliciting their prior knowledge to improve the models of the drugs. This is difficult because the goals may be tacit, uncertain and evolving. Another postdoc will develop the new molecular models for drugs, to which you are welcome to contribute. The work will augment and consolidate existing well-developed research pipelines of the supervisors. The selected student may also have the opportunity to work with and visit our collaborators at leading pharma companies and academic groups in Europe, USA, and Canada. This will be a transformative project, resulting in a virtual drug design laboratory.
Key methods we will need: probabilistic modelling and Bayesian inference, multi-agent modelling, sequential experimental design, POMDPs, reinforcement learning and inverse reinforcement learning. We expect applicants to master some of these, or be exceptionally eager and quick learners.
 Celikok et al. Teaching to Learn: Sequential Teaching of Agents with Inner State. arXiv:2009.06227
 Mikkola et al. Projective Preferential Bayesian Optimization. ICML 2020
 Peltola et al. Machine Teaching of Active Sequential Learners. NeurIPS 2019
 Kangasrääsiö et al. Parameter inference for computational cognitive models with Approximate Bayesian Computation. Cognitive Science 43 (2019): e12738.
Project 16: Machine Learning for Health (ML4H)
Supervisor: Prof. Pekka Marttinen (Department of Computer Science, Aalto University)
Recent years have witnessed accumulation of massive amounts of health related data, enabling researchers to address a range questions 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 integrating noisy data from multiple heterogeneous data sources, going beyond correlation to learn about causality, 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 machine learning principles: Bayesian neural networks, deep latent variable models, interactive machine learning, attention, reinforcement learning, and natural language processing. 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 17: 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. It allows making sure that published machine learning models do not leak sensitive training data, and allows creating synthetic data with strong anonymity guarantees for sharing sensitive data. In this project, you will join our group in developing new learning methods operating under these guarantees. Our work focuses on Bayesian machine learning but also covers 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 18: Methods for large scale fault-tolerant quantum computing
Supervisor: Prof. Alexandru Paler (Department of Computer Science, Aalto University)
I am looking for scientists to research and develop scalable methods and software necessary at different layers of the quantum software stack of fault-tolerant quantum computers. The research topics range from decoders for quantum error-correcting codes, compilers for large scale fault-tolerant quantum circuits and verification methods of optimised circuits. For an architectural perspective please refer to this paper.
This doctoral student position is funded for three years. The candidate is expected to show a strong motivation and commitment to research by contributing to the realization of the tools that are developed at the department. Requirements:
- Master in Computer Science/Engineering or Physics
- Excellent programming skills in C++ and Python
- Preferable, experience with one of the following: Qiskit, Cirq, PennyLane
- Ability to work in a research team
- Good skills in preparation of research manuscripts
Project 19: Data science for mental health and well-being
Supervisor: Dr. Talayeh Aledavood (Department of Computer Science, Aalto University)
People leave digital traces behind while interacting with different devices and online platforms such as smartphones, fitness trackers, and social media. These interactions with technology are producing large amounts of data of our daily activities. Studying these traces, we extract behavioral patterns to understand people’s behavior as well as the state of their health and well-being.
In the past 5 years in collaboration with clinicians we have run a study which has collected data from over 100 patients with mental disorders using devices such as participants’ smartphones and fitness trackers. The goal in this project is to continuously quantify the behavior and state of these patients using the collected data to find new behavioral markers for these disorders. We aim to predict the future state and mood of the patients. In other studies, we collect data from the general population, either in small scale (i.e. hundreds of people) from people’s personal devices (e.g. https://corona.cs.aalto.fi/) or from large number of people (up to millions) from social media platforms. The goal is to quantify behavioral patterns of users and changes in these patterns due to a disruption, e.g. the COVID-19 pandemic.
The exact projects can be negotiated based on the interests and expertise of the student. In these projects we will use methods that are best fitting to each problem and will collaborate with other research teams with expertise in psychiatry, psychology, or experts in specific methods in statistical and machine learning. This position is especially suitable for an ambitious candidate who wants to work closely with their supervisor in a small research group and ideally has an academic career in mind. The supervisor is committed to the mentoring of junior scholars in their group in all aspects of their career, for example building an international network of collaborators with leading scientists.