Aalto University | Summer employee positions at the Department of Computer Science 2025

The Department of Computer Science is looking for summer employees

We at the Department of Computer Science want to offer motivated students a chance to work on interesting research topics with us. We are looking for BSc or MSc degree students at Aalto or other universities to work with us during the summer 2025. If you have enjoyed your studies and want to learn more about computer science, this might be your place. We do not expect you to have previous research experience; this could be the start of your bright researcher career! You will be supported by other summer employees, doctoral students and postdocs at the department.

Ready to apply?

See the complete list of the available topics below (will be fully updated by January 2nd)) and choose the topic(s) (max. 5) that interest you the most. Please indicate them in order of preference in the relevant section on the application form.

Please submit your application through our recruitment system. The application form will open on January 7th and close on January 31st, 2025, at 23:59 Finnish time (UTC +2).

Link to the application form: CS Summer recruitment 2025

Are you an international student or coming from abroad?

Please check the Aalto Science Institute AScI internship programme for international summer employees. https://www.aalto.fi/en/aalto-science-institute-asci/how-to-apply-to-the-asci-international-summer-research-programme

AScI arranges activities for international summer employees who have applied through their call and helps in finding an apartment in Espoo.

More information

If you have questions regarding applying, please contact Susanna Holma from HR Team. firstname.lastname@aalto.fi.

Summer employee topics 2025

Topics are listed here and will be fully updated by January 7th at the latest.

1. Diplomityöntekijä terveysteknologian alalle
   • Supervisor: Sari Kujala
   • Contact info: sari.kujala@aalto.fi
   • Number of open positions: 1
   • Diplomityön aiheena on ikäihmisen omaishoitajan digitaaliset ratkaisut. Vaatimuksena on HCI-alan menetelmien hallitseminen ja suomen kielen osaaminen. Pääaineena mielellään HCI tai Informaatioverkostot ja tutkimusmenetelmäkurssin suoritus katsotaan eduksi.
2. Designing for Developer Experience
   • Supervisor: Fabian Fagerholm
   • Contact info: fabian.fagerholm@aalto.fi
   • Number of open positions: 1-3
   • The Mind and Software research group is looking for skilled and motivated research assistants to contribute to our research on developer experience. Developer experience refers to the cognitive, motivational, and affective experience that software developers have while developing software. We design methods and tools for studying and assessing developer experience in various contexts in modern software development.
   • We are looking for people to perform different kinds of tasks, and you may be involved in one or more of them in different combinations. In research-oriented tasks, you participate in planning and conducting empirical studies with software developers, collecting data through interviews, observation, or instrumentation, analysing data using qualitative and quantitative methods, and reviewing existing scientific literature. In technically oriented tasks, you participate in developing software for developer experience measurement. Finally, in design-oriented tasks, you contribute user interface and visual designs for research materials, questionnaires, and measurement tool user interfaces.
3. Methods and models for Continuous Experimentation
   • Supervisor: Fabian Fagerholm
   • Contact info: fabian.fagerholm@aalto.fi
   • Number of open positions: 1-3
   • The Mind and Software research group is looking for skilled and motivated research assistants to contribute to our research on Continuous Experimentation (CE). CE is an approach where field experiments with real users inform software product development, for example, through A/B testing. We are investigating methods and models for various aspects of the CE process and for different kinds of organisations and products.
   • In this project, you would contribute to ongoing research to support development of ways to identify and specify what to test in experiments, how to produce representative experiment objects to use in the experiments, and to understand how humans make decisions in the experimentation process. You would participate in conducting empirical studies with software practitioners, which may involve interviews, questionnaires, or task-oriented studies and the analysis of data from these. The position can be combined with a Master’s thesis if you are enrolled at Aalto University.
4. Causal approach for design optimization problem in the presence of distribution shift by leveraging human feedback
   • Supervisor: Zahra Rahiminasab, Samuel Kaski
   • Contact info: zahra.rahiminasab@aalto.fi
   • Number of open positions: 1
   • In a design optimization process a machine learning model tries to find an optimal design based on some predefined metric. However, the machine learning model is usually trained on known source environments and must be generalized to the new target environment. In this case, the problem is formulated as a domain adaptation problem. Information regarding domain shifts in environment distribution can be provided by human feedback. Causal modeling formalizes required assumptions to establish causal relationships between treatment (covariates) and outcomes. Causal exchangeability is one of the main assumptions in a classic causal modeling setting. Unconfoundedness means there is no unobserved shared cause between treatment and outcome that can affect both. However, such an assumption is violated in practice, and an unobserved confounder can be a source of distribution shift. While there are approaches that address unobserved confounders in causal literature, they are usually based on restricting assumptions that do not hold in practice. In this internship, we empirically explore the violation of such assumptions and their impacts on causal identifiability.
5. Leveraging graph neural networks to understand Finnish healthcare data
   • Supervisor: Jorge Loría, Samuel Kaski
   • Contact info: jorge.loria@aalto.fi
   • Number of open positions: 1
   • Graph neural networks (GNNs) facilitate understanding complex relationships between variables in many areas of engineering and science (Scarselli, et al. 2009). An exciting application of these models is to understand how the graphs of physicians and patients can affect healthcare outcomes. Specifically, the intern will investigate prescription patterns of doctors, while leveraging characteristics of patients and clinicians. Understanding these complex relationships with GNNs will shed light in possible over(under)prescriptions, as well as help describe relationships between patient characteristics and doctor behaviors.
6. Improving on Upper confidence Reinforcement Learning (UCRL) leveraging human insight
   • Supervisor: Mahsa Asadi, Samuel Kaski
   • Contact info: mahsa.asadi@aalto.fi
   • Number of open positions: 1
   • UCRL is a model-based reinforcement learning (RL) approach with infinite horizon trajectory and optimizing the average reward criterion. This is one of the important baselines providing theoretical guarantees on the algorithm regret. There are various works in the literature extending this approach in order to improve the regret bound. However, there is not much work considering human in the loop of learning and analyzing the RL agent performance. In this internship, we are going to explore some of the extensions of UCRL, come up with useful human feedback, analyze our proposed algorithm and implement the idea and show that it works in practice as well.
7. Deep ensemble based approaches for causal discovery
   • Supervisor: Nazaal Ibrahim, Samuel Kaski
   • Contact info: nazaal.ibrahim@aalto.fi
   • Number of open positions: 1
   • Causal models, specifically causal graphs, are essential to predict how a system behaves under interventions. This is important in applications ranging from healthcare to economics. However, learning causal models from data is a nontrivial task. Many probabilistic approaches for causal model learning exist, which rely on Bayesian inference methods such as Stein Variational Gradient Descent and Stochastic Gradient Markov Chain Monte Carlo. Rather than using such inference methods, this project will be about investigating the use of deep ensemble style approaches to learn causal models. If time permits, there is room to focus on incorporating extra information sources such as human feedback, and the use of these methods in Bayesian experimental design settings.
8. Accelerating Bayesian inference with modern deep learning techniques
   • Supervisor: Daolang Huang, Samuel Kaski
   • Contact info: daolang.huang@aalto.fi
   • Number of open positions: 1
   • Pre-trained large language models have advanced rapidly in recent years, yet the intersection of traditional Bayesian inference and neural networks remains in its early stages of development. This project explores whether we can develop powerful pre-trained models to accelerate Bayesian inference tasks. The primary focus will be investigating novel amortized inference methods by leveraging cutting-edge neural network techniques to significantly speed up inference in various Bayesian tasks. These tasks include Bayesian optimization, Bayesian experimental design, and simulation-based inference. The intern will have the freedom to choose and explore topics based on their interests. This work builds on our group’s prior research. Collaborators will have the opportunity to contribute directly to projects aimed at publishing in leading venues.
9. Expert-driven Conformal Prediction Set Refinement Improves Robustness in Bayesian Optimization
   • Supervisor: Marshal Sinaga, Samuel Kaski
   • Contact info: marshal.sinaga@aalto.fi
   • Number of open positions: 1
   • Bayesian Optimization (BO) is widely regarded as a gold-standard method for optimizing expensive black-box functions. To enhance its robustness against model misspecification and covariate shifts, several studies have introduced conformal prediction sets as a promising approach. However, existing methods predominantly rely on purely data-driven techniques. This internship seeks to explore the integration of human expert knowledge to refine or reconstruct conformal prediction sets, leveraging the domain-specific insights that experts provide to improve robustness in BO. You will have the freedom to explore the experimental or theoretical aspects.
10. Improving the robustness of high-dimensional data modeling with shrinkage priors and projective predictive inference
    • Supervisor: Ersin Yilmaz, Samuel Kaski
    • Contact info: ersin.yilmaz@aalto.fi
    • Number of open positions: 1
    • In high-dimensional data modeling, a critical issue is the presence of weak effects —predictors with small yet meaningful contributions to the response variable—which are often overlooked or excluded by traditional methods. These weak signals can hold valuable information, especially in complex systems where subtle patterns drive critical insights. Projection predictive inference offers a robust solution to the challenges of considering weak effects and interpretability in high-dimensional settings, particularly in “small n, large p” problems. This approach, as detailed in Piironen et al., separates predictive modeling and feature selection into two stages: first, constructing a reference model using all predictors for maximum accuracy, and second, projecting this onto simpler submodels to retain predictive power while reducing complexity. Compared to well-known regularization methods like adaptive Lasso or Elastic Net, projection predictive inference ensures optimal trade-offs between sparsity and prediction by explicitly preserving the information from the reference model that involves the weak effects. It is possible to explore extensions of this method, including its integration to multi-output regression to capture dependencies among outputs, integration with latent factor models for handling structured data, and the incorporation of advanced shrinkage priors such as the horseshoe+ to improve sparsity and stability. These methodological developments can expand its adaptability to high-dimensional problems where the weak effects and interpretability are critical.

Source and more job positions at https://www.aalto.fi/en/department-of-computer-science/summer-employee-positions-at-the-department-of-computer-science-2025