Research Internships

Large Language Model to extract from digitized archives

Supervisor: Dilek Dustegor.
Status: Available.
Date: 11/04/2025.
This project aims to conduct a pilot study utilizing Large Language Models (LLMs) to extract and analyze data on historical tortoiseshell trade from the Dutch East India Company archives. The intern is expected to build an LLM solution to extract all information related to marine life, including but not limited to location, quantity, date, type. This solution would involve dealing with large machine-translated files, serving LLM, prompt engineering, and documentation of the process. This is part of a larger project, where the extracted data will be analyzed within the framework of historical ecology, focusing on quantities, temporal patterns, and geographic distribution (in collaboration with Willemien de Kock (Faculty of Arts) and Emin Tatar (CIT)). \\ References:

"5 million scans VOC archives online and searchable".

DiTEC project- Building a collection of Graph Self-Supervised Learning tasks

Supervisor: Huy Truong.
Status: Available.
Date: 03/04/2025.
Self-supervised learning (SSL) has shown great potential in enhancing the capabilities of large foundation models, but its application to graph modalities remains underexplored. This project aims to investigate popular SSL tasks across node-level, link-level, and graph-level challenges, as well as more complex graph representation learning approaches. As a researcher of the project, the candidate will develop a framework that enables users to train deep learning models using these tasks on independent datasets. The final deliverables will include the implementation code and a report detailing the problem and the proposed solution. The ideal candidate should have a background in machine learning, and experience with at least one deep learning framework.
References:

DiTEC project- Inverse problem in Water Distribution Networks

Supervisor: Huy Truong.
Status: Available.
Date: 03/04/2025.
Water researchers have relied on simulations to monitor the behavior of Water Distribution Networks. These simulations require a comprehensive set of parameters- such as elevation, demand, and pipe diameters-to determine hydraulic states accurately. This increases labor cost, time consumption and, therefore, poses a significant challenge. But what if we could reverse the process and let AI infer the missing pieces? Building on this idea, the project explores an innovative approach: leveraging data-driven deep learning methods to predict initial input conditions based on available output states. As a researcher on this project, the candidate will select and train a cutting-edge Graph Neural Network on a massive dataset. As a result, the model should be able to predict initial conditions while considering the structural and physical constraints. The candidate will submit the implementation code and a report detailing the problem and the proposed solution. The ideal candidate should have a background in machine learning and be familiar with at least one deep-learning framework.
References:

Truong, Huy, et al. "DiTEC-WDN: A Large-Scale Dataset of Hydraulic Scenarios across Multiple Water Distribution Networks." (2025).

DiTEC project- Bio-inspired Water Network Design

Supervisor: Huy Truong.
Status: Available.
Date: 03/04/2025.
Designing Water Distribution Networks (WDN) has been portrayed as a complex, labor, and time-consuming process. To alleviate this, the project aims to automate the design using Evolution Strategy (ES). In particular, these algorithms should search and optimize values of hydraulic parameters, such as nodal elevation, pump speed, and pipe length, to construct a complete simulation configuration. This configuration should follow the local, structural, and physical restrictions (i.e., multi-objective optimization). As a researcher on this project, the candidate will explore an ES framework to develop the optimization algorithm and apply it to a water distribution domain. As such, the candidate should be familiar with machine-learning experiments. As deliverables, the candidate should submit the report and implementation code that generates optimized configurations. These configurations will help water researchers simulate, analyze, and understand the WDN’s behavior and enhance the monitoring capability of these systems in practice.
References:

Can we train a Neural Network with Forward-Forward “harmoniously”?

Supervisor: Huy Truong.
Status: Available.
Date: 03/04/2025.
Back Propagation(BP) is a de facto approach to training neural network models. Nevertheless, it is biologically implausible and requires complete knowledge (i.e., tracks the entire flow of information from start to end of a model) to perform a backward pass. Instead, an alternative approach called Forward-Forward(FF) can replace the backward pass with an additional forward one and update the model weights in an unsupervised fashion. In particular, FF performs forward passes using positive and negative inputs, respectively, and, therefore, employs the difference between the two activation versions at each layer in the neural network to compute the loss and update weights. Here, the project studies the behavior of FF employing different losses: (1) cross-entropy and (2) harmonic loss. Also, it is valuable to study the relevance between harmonic loss and FF in terms of distance metrics or geometric properties in an embedding space. As a deliverable, the candidate should submit a detailed report and implementation code. For primary requirements, the candidate should be familiar with one of the deep learning frameworks and have experience in setting up machine learning experiments.
References:

Leveraging Structural Similarity for Performance Estimation of Deep Learning Training Jobs

Supervisor: Mahmoud Alasmar.
Status: Available.
Date: 04/04/2025.
Deep learning (DL) workload-aware schedulers make decisions based on performance data collected through a process called profiling. However, profiling each job individually is computationally expensive, reducing the practicality of such approaches. Fortunately, DL models exhibit structural similarities that can be leveraged to develop alternative methods for performance estimation. One promising approach involves representing DL models as graphs and measuring their similarity using Graph Edit Distance (GED) [1]. By analyzing the structural similarities between models, we can potentially predict the performance of one model based on the known performance of another, reducing the need for extensive profiling. In this project, you will: Study and implement the similarity matching mechanism proposed in [2], compare runtime performance of similar DL models, focusing on key metrics such as GPU utilization and power consumption, and investigate the relationship between model similarity and performance predictability, trying to answer the following question: Given two similar DL models and the performance of one, what can we infer about the performance of the other? You will work with a selected set of DL training models, and performance metrics will be collected using Nvidia's GPU profiling tools, such as DCGM.

References:

Metastable Failure Prediction Using ML Techniques

Supervisor: Kawsar Haghshenas, Heerko Groefsema.
Status: Taken (unavailable).
Date: 04/05/2023.
Metastable failures have recently been introduced as a new class of failures in distributed systems [1-2]. As defined in [1], metastable failures occur in open systems that lack control over the source of load, where a trigger causes the system to enter the state of permanent overhead with a low throughput that persists even when the trigger is no longer active. Unlike many other failures attributed to hardware malfunctions or software bugs, the root cause of metastable failures is not a specific hardware failure or a software bug. Consequently, metastable failures are hard to predict and incur the substantial human engineering efforts required due to the inherent difficulty in achieving automated recovery. In [2], three example applications on which metastable failures are experimentally reproduced are presented. In this project, we aim to explore using various ML techniques and the data set created by the experiments in [2], for metastable failures prediction.

Graph Neural Network for Leakage Detection in Water Distribution Networks

Supervisor: Dilek Dustegor, Andres Tello, Huy Truong.
Status: Taken (unavailable).
Date: 12/04/2023.
The project is related to leak detection and localization in water network. This is mainly an implementation project. A recent study [1] has proposed a leakage detection and localization method in water distribution network, combining data-driven techniques (more specifically, graph neural-network, GNN) and model-based logic. The project consists in implementing / reproducing the proposed methodology. However, the GNN-based reconstruction algorithm will be replaced with our own model (Andres and Huy’s model). The corresponding code will be provided for this module.

In the ideal case, the student is expected to:
1- study the paper [1] and implement the proposed method,
2- develop a working understanding of the Pytorch Geometric library (which is used for the implementation of Graph Neural Networks)
3- replace one module with an existing GNN model,
4- perform the necessary optimization of hyperparameters
5- design a set of experiments to validate the leak detection approach
6- run the experiments to collect the performances
7- compile a comparative table
8- discuss the obtained results
However, if time becomes an issue, the focus will be on above listed tasks 1-3. The main deliverables will be a written report describing your work and the documented code. An oral presentation will conclude this internship.
References:

Research Internship on ECiDA Project

Contact: Mostafa Hadadian.
Status: available.
Date: 15/01/2024.
ECiDA aims to narrow the gap between the data scientists who experiment with data and try new models, and the production environment wherein data science models should eventually run and exhibit consistent behavior. Our goal is to provide a solution that can also be applied to existing data processing platforms.

The infrastructure of ECiDA revolves around containerized components, in which each computational component is responsible for a single step in the data science pipeline. For this, we rely on technologies such as Docker, Kubernetes, and Kafka to containerize, orchestrate, and enable communication between the computational components.

The topics that you can apply include but are not limited to:

Monitoring systems
Web development
Container Orchestration
CI/CD Pipelines
Network Service Mesh

Automated reasoning using automated planning

Supervisor: Heerko Groefsema.
Status: available.
Date: 26/10/2021.
Verification entails proving or disproving the correctness of a system model with respect to its specification. Such specifications are often expressed using formal methods of mathematics such as temporal logics. To obtain information on successor states in system models, it is possible to rewrite temporal logic expressions using semantic equivalences and expansion laws. Automated planning is an artificial intelligence technique that aims to find an optimal set of actions which together accomplish a predetermined goal. The question for the student then is, can we use automated planning to obtain the possible expanded logic expressions and can we obtain the optimal expanded expression?

Make a difference in Energy Transition with Machine Learning

Contact: Frank Blaauw..
Status: available.
Location: eWEning star.
Date: 01/06/2021.
eWEning star is a “fresh from the oven” Start-Up, which is currently developing a discovery tool that serves stakeholders in the renewable energy sector with relevant scientific information regarding renewable energy. Currently people in this sector use key-word based search queries in order to find scientific papers and reports, but with eWEning star’s concept, these papers are smartly categorized, saving users a lot of time and nerves. By making the search process more efficient we can make the energy transition towards renewables faster! Currently we have around 900 documents that are manually categorized in three different ways: (i) perspective, (ii) position in value chain, and (iii) geographical location. Combined, we have created 15 categories. Depending on the length of your internship, it is possible to work on these all, or choose one out of the three options. While this manual approach is feasible for a small number of papers, it does not scale well. Our aim is to apply Machine Learning to improve this process. We expect that machine learning can provide us with a fast solution for categorizing already published papers according to eWEning star concept. You are given the freedom to design, develop and test a process which leads to the automated categorization. You have a background in Data Science and/or computer science, and you have natural curiosity for solving issues. You aren’t afraid to ask questions if you seem to “hit the wall”, but are capable of working independently. Some entrepreneurial mentality is a benefit as eWEning star is a Start-Up. Good communication skills are needed towards non-technical founder.

Researchable in-company internship

Contact: Frank Blaauw.
Status: available.
Location: Researchable B.V.
Date: 01/06/2021.
Researchable B.V. is a small startup located in Groningen. They aim to improve science by developing software in the early phases of research projects (e.g. developing software to collect data, or automate other parts of research) and at the final phase of research projects (i.e., the valorisation of research). During this internship, the student will be part of the Researchable team, and work on various projects that they are currently running. Their office is located on Zernike.

Have your own project suggestions?

We are available to supervise projects on various aspects of distributed systems, in particular involving

Service-Oriented and Cloud Computing
Pervasive Computing and Smart Environments
Network Centric Real-time Analytics
Energy Distribution Infrastructures
Adaptive Communication Middleware

If you have an idea of a specific project or would like to work generally in a specific area, please let us know about it and we can then narrow the project down.

Please feel free to contact us to discuss specific topics and options.

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