Course syllabus
Can we interest you in a thesis project in computational science?
On this page we collect suggestions for projects covering artificial neural networks, systems biology, bionanophysics, quantum computing or complex statistical modelling.
Please scroll down for the newest projects
Students from physics, computational science and other disciplines are invited to do their thesis work with us at Computational Science for Health and Environment (COSHE). The COSHE theme was formed at CEC in 2023 by the members of Computational Biology and Biological Physics (CBBP, formerly at Astronomy and Theoretical Physics) and the Uncertainty and Evidence Lab.
Both bachelor and master students are welcome to do their projects in one of our research groups. We offer a strongly interdisciplinary research environment where staff and students have a background in diverse subjects that include physics, computer science, medicine and mathematics and environmental science. Our thesis students are integrated into the group both scientifically and socially for the duration of the project, and gain valuable experiences and connections. Many of our master project students are subsequently recruited to PhD positions, either with us or elsewhere.
Below you will find descriptions of possible topics for your degree project. You can also contact the teachers directly and ask about possible projects. Many examples of earlier theses are available from LUP Student papers.
This does not apply for students in computational science and in envrionmental science. We currently do not have our own courses for bachelor or master projects, but act as external supervisors in collaboration with other departments. Thus, you contact us to discuss possible projects and the course coordinator for your regular degree project course (e.g. FYSK03, FYSM33 or BERM01). The course coordinator needs to determine that the project fits within your main subject and, if needed, will also help you find a formal supervisor in their department.
Project descriptions
Quantum computing: Folding and design of toy proteins
Simplified lattice-based models provide a means for exploring the basics of the sequence–structure relationship of proteins. One then needs to solve discrete optimization problems, which, despite the simplicity of the models, become computationally challenging for large proteins. Quantum computing offers a potentially fast approach to difficult optimization problems. The aim of this project is to explore quantum computing based methods for solving lattice protein problems.
Contact/supervisor: Anders Irbäck (anders.irback@cec.lu.se)
Recommended knowledge: Statistical mechanics corresponding to FYTN15 and programming experience. Knowledge in theoretical biophysics and computational physics (corresponding to FYTN05/BERN05 and FYTN03, respectively) is helpful but not required.
Language: English or Swedish.
Bionanophysics: Parameter inference from single-molecule trajectories
Single-molecule experiments, such as single particle tracking
(SPT), enables precise quantitative analysis beyond ensemble averages. However, when analyzing experimental SPT trajectories, extracting kinetic rates and diffusion constants is made difficult due to different types of experimental noise. Two common such noise types are: motion blur (the molecules move during each sampling time) and localization noise (not enough photons were detected for accurate precision).
In this project you will dwelve into using Bayesian data anaysis for biomolecule trajectories. You will (i) simulate realistic movies of moving biomolecules as obtained in wide-field fluorescence microscopy setups, (ii) from the movies extract trajectories, and (iii) from the trajectories use state-of-the-art Bayesian methods for estimating parameters. You will include all experimental noise components, and address how including or neglecting these affects your parameter estimates.
Contact/supervisor: Tobias Ambjörnsson (tobias.ambjornsson@cec.lu.se)
Recommended knowledge: Computational physics corresponding to FYTN03 (Computational Physics) and programming experience.
Language: English or Swedish.
Bionanophysics: DNA melting in forensics
The sequence of base pairs of DNA molecules is the genetic blueprint of any living being. A DNA molecule forms a double-helix where the complementary interactions between bases strike a balance between stability (protecting the integrity of the double helix) and accessibility (for instance, the bases need to be accessible when reading the DNA sequence). Because of the non-covalent nature of the bonds between bases, the DNA molecules “melt” (base pairs get separated) when heated above room temperature. The associated melting curve (fraction of “melted” DNA base pairs as a function of temperature) is sensitive to DNA sequence, with local melting temperatures that range from 60 to 110 °C. The sensitivity of DNA melting to DNA sequence can be used in DNA fingerprinting. By performing optical measurements, the fraction of melting base pairs as a function of temperature can be extracted. Since DNA melting curves are sensitive to DNA sequence, DNA fingerprinting based on DNA melting has the potential to complement standard DNA sequencing.
As an example, DNA molecules are subject to a process called methylation, where base pairs undergo chemical modifications. These modifications alter the interaction energy between bases and, as a consequence, the DNA melting curves are sensitive to the methylation fraction and position of the methylation events. Since methylation events are dependent on lifestyle and age as well as random contributions, two individuals with identical DNA sequences (twins) will have different methylation patterns. Therefore, the associated DNA melting curves will differ, a fact which has been used in forensics.
In this project you will numerically investigate the effect of methylation with an established model for DNA melting (the Poland–Scheraga model). The work will be performed in collaboration with theoretical and experimental partners at Umeå and Uppsala Universities.
Contact/supervisor: Tobias Ambjörnsson (tobias.ambjornsson@cec.lu.se)
Recommended knowledge: Good programming experience and knowledge of statistical mechanics corresponding to FYTN05/BERN05.
Language: English or Swedish.
Machine learning: Microplastics identification
Microplastics are small particles of plastics, ranging in size from micrometers to millimeters. As the wide range of plastics used industrially and in households are shed and broken down, large quantities of microplastics end up in our waste. European farmland is estimated to receive 31,000–42,000 tons annually from biosolids and composts, and this is a growing concern for both human health and then environment.
Many machine learning methods have been applied to spectroscopic and imaging data for the purpose of identification and quantification of microplastics. We have a dataset where biological waste has been treated to break down organic materials, and the remaining solids have been filtered and imaged. In these microscopy images, particles that may be plastics can be seen and counted, and IR spectroscopy methods can be used to determine what types of plastics they are.
It is desirable to automate the task of identifying potential plastics particles, but this is not trivial because of the complex structure of the filters themselves. The image to the right here shows an example of unsuccessful image segmentation by a convolutional neural network; the areas marked by the network contain nothing. Your task in this project is to develop and train a neural network model that successfully finds regions/points that are suitable candidates for further exploration by spectroscopic measurements.
Contact/supervisor: Carl Troein (carl.troein@cec.lu.se)
Recommended knowledge: Programming skills and knowledge of artificial neural networks and deep learning corresponding to FYTN14/BERN04.
Language: English or Swedish.
Machine learning: Explainable neural networks
An artificial neural network (ANN) is a powerful and flexible machine learning tool. It has gained popularity in recent years due to its connection to deep learning and its success in e.g. image analysis and language processing. When using neural networks for e.g. medical diagnosis or outcome prediction, it is of vital importance to be able to explain the reasoning behind an advice. Generally this is difficult for non-linear machine learning methods. This topic of unmasking the “black boxes” has become even more important with the present focus on deep learning.
In this project you will explore different methods for explaining the operation of complex neural networks models.
Contact/supervisor: Mattias Ohlsson (mattias.ohlsson@cec.lu.se)
Recommended knowledge: Programming skills. Also it is advisable to have knowledge of artificial neural networks and deep learning corresponding to FYTN14/BERN04.
Language: English or Swedish.
Machine learning: Identifying vascular invasion in breast cancer by deep learning on histopathological slides
Invasion of cancer cells from the tumor into surrounding vessels, vascular invasion (VI), is a prerequisite for metastatic spread and a well-established prognostic factor in solid cancers including breast cancer. The identification of VI is currently done by clinical pathologists on histopathological slides based on morphological criteria. Digital pathology has paved the way for training deep learning models, enabling automatic identification of tumor characteristics, and is moving towards clinical application. Image analysis of VI has been successful in some cancer types, but is yet not implemented in the clinics. There is no data from breast cancer on digital pathology and VI.
The aim of the project is to employ deep learning techniques on histopathological slides to identify vascular invasion using both an annotated region of interests and whole slide sections. The material includes digitalized whole section slides from 500 patients with well-annotated tabular data in which 14% are diagnosed with presence of vascular invasion. The pathological assessment of VI serves as ground truth of the model. The project may include transfer of the algorithm from whole slide sections to biopsies enabling preoperative diagnosis on VI.
Contact/supervisor: Patrik Edén (patrik.eden@cec.lu.se) and Lisa Rydén (lisa.ryden@med.lu.se)
Recommended knowledge: Neural networks and deep learning corresponding to FYTN14/BERN04, programming skills.
Language: English or Swedish.
Machine learning: Methods for skin tumour classification and delineation
We are developing machine learning tools that are combined with image segmentation and active contour algorithms to determine the size and type of skin tumours. This project is conducted in collaboration with the Photacoustic Center at Eye Clinic Lund, who provide a multitude of tumour imaging data.
The aim of this project is to predict the size of skin tumours from hyperspectral and photoacoustic images in 3D. It also aims at classifying the tumours based on their hyperspectral signature.
Contact/supervisor: Victor Olariu (victor.olariu@cec.lu.se) and Patrik Edén (patrik.eden@cec.lu.se)
Recommended knowledge: Neural networks and deep learning corresponding to FYTN14/BERN04, programming skills.
Language: English or Swedish.
Systems biology: Gene regulatory network models for cell reprogramming and differentiation
We offer a range of projects on developing computational models for gene networks that govern skin cell reprogramming to stem cells or direct reprogramming to specific types of neurons. For example, finding new better ways to produce transplantable neurons is highly relevant for patients that suffer from Parkinson’s or Alzheimer's diseases. Single cell data are available from our established collaborations with various wet labs.
These projects involve data analysis and model optimisation along with deterministic and stochastic model simulations to predict solutions for improving the production of target cells.
Contact/supervisor: Victor Olariu (victor.olariu@cec.lu.se)
Recommended knowledge: Programming skills and systems biology (corresponding to FYTN12/BERN06). It is also advisable to have knowledge in computational physics or computational science (corresponding to FYTN03 or BERN01).
Language: English or Swedish.
Systems biology: Production of plastic building blocks from sugar
The excessive use of single-use plastics has resulted in severe environmental, social and health consequences, partly because of their longevity in nature due to a lack of natural degradation systems. A promising solution is to develop a new generation of plastics that are bio-based and recyclable or compostable. Examples of raw materials with the potential to become building blocks for this new generation of plastics are 4-hydroxybutyrate (4-HB) and 1,4-butanediol (1,4-BDO). The production of environmentally friendly plastics from these biomaterials is hampered by their limited industrial production.
In this project, you will work with modelling microbial cell factories and improve our knowledge on how to increase bio-based production of the molecular building blocks 4-HB and/or 1,4-BDO. If you are interested in translating biological challenges into computational solutions and vice versa and have some knowledge of biochemistry, bioinformatics or computational sciences, this may be the project for you. The project is carried out in collaboration with the Division of Biotechnology at the Department of Chemistry.
Contact/supervisor: Victor Olariu (victor.olariu@cec.lu.se) and Carl Troein (carl.troein@cec.lu.se)
Recommended knowledge: Programming skills and systems biology (corresponding to FYTN12/BERN06).
Language: English.
Complex statistical modelling: Uncertainty quantification and integration of diverse evidence in assessment models
Statistical modelling for evidence synthesis aims to build data generative models to assimilate data from multiple lines of evidence when making conclusions on a parameter of interest. In an applied context, there can also be a requirement to consider the combined impact of sources of uncertainty on the conclusion. This can include Bayesian evidence synthesis, bias adjustement and ways to integrate expert judgements with modelling. We offer theses projects developing and applying computational methods with applications on human health risk assessment, ecological risk assessment or other types of assessments. I am open to supervise projects with different applications, where you have a second supervisor that is an expert on the application. Applications can be human health risk assessment (e.g. design of testing strategies), biodiversity impact assessment (with counter factual policy analysis), ecological-economic modelling (calibration and policy analysis).
Recommended knowledge: Programming skills. Probability theory, Bayesian statistical modelling e.g. MCMC sampling.
Contact/supervisor: Ullrika Sahlin (ullrika.sahlin@cec.lu.se)
Complex statistical modelling: Network meta-analysis to evaluate effect of heterogeneous farming practices on biodiversity
Statistical modelling for evidence synthesis aims to build data generative models to assimilate data from multiple lines of evidence when making conclusions on a parameter of interest. This project is to perform a network meta analysis using data from a global data base on heterogeneous farming practices to evaluate its impact on biodiversity.
Contact/supervisor: Ullrika Sahlin (ullrika.sahlin@cec.lu.se)
Recommended knowledge: Programming skills. Knowledge in statistical methods, probability theory, Bayesian analysis and MCMC sampling.
Suitable for students in the master program in applied computational science.
Language: English or Swedish.
Complex statistical modelling: Robust decision making in climate adaptation
The aim of this project is to build a decision model using climate data (historical and projections) of extreme weather events to inform climate adaptation decisions. The project includes parameterization of Hawkes process based on precipitation data.
Contact/supervisors: Ullrika Sahlin (ullrika.sahlin@cec.lu.se)
Recommended knowledge: Programming skills. Bayesian statistical modelling. Decision theory.
Language: English or Swedish.
Complex statistical modelling: Bayesian modelling and machine learning for air pollution epidemiology
The aim of this project is to develop an existing methodology to evaluate the effect from air pollution on human health based on data from epidemiological studies.
Contact/supervisors: Ullrika Sahlin (ullrika.sahlin@cec.lu.se) and Anna Oudin (anna.oudin@med.lu.se)
Recommended knowledge: Programming skills. Knowledge in Bayesian statistical modelling and machine learning.
Language: English or Swedish.
Geophysics: Models of geomagnetic field and core flow variations over the past 9000 years
By studying geomagnetic field variations at the Earth's surface, we can learn more about the deep Earth where the field is generated. We offer a variety of projects on developing models of how the geomagnetic field and core surface flow have varied over the past 9000 years. These range from purely data-driven statistical geomagnetic field models based on different types of datasets, each associated with their unique challenges, to more physics driven core field and core flow models informed by statistics derived from numerical geodynamo simulations. Potential project aims include solving problems related to chronological data uncertainties through implementation of more efficient MCMC applications and exploring alternative model parameterizations for hypothesis testing.
Contact/supervisor: Andreas Nilsson (andreas.nilsson@geol.lu.se) and Anders Irbäck (anders.irback@cec.lu.se).
Language: English or Swedish.
Geophysics: Reconstructing solar activity and solar storms from ice core and tree ring data
Cosmogenic radionuclide records from ice cores (10Be, 36Cl) and tree rings (14C) provide information about solar and geomagnetic shielding of galactic cosmic rays far back into the past. However, such indirect “proxy data” is also influenced by the geochemical behaviour of these radionuclides such as e.g. climate and weather effects on their atmospheric transport. The challenge is to reliably extract the solar activity and solar storm information from noisy data. However, we can make use of the fact that we usually have parallel records from different ice cores and tree rings allowing us to increase the signal to noise. Furthermore, we can also use our knowledge about the typical behaviour of solar activity and known confounding effects on the radionuclides. Potential projects aim to combine such information into a statistical model to improve solar activity reconstructions and to push the limits of the identification of large solar storms in the past.
Contact/supervisor: Raimund Muscheler (raimund.muscheler@geol.lu.se), Anders Irbäck (anders.irback@cec.lu.se), and Ullrika Sahlin (ullrika.sahlin@cec.lu.se)
Language: English or Swedish.
Statistical Learning: Development of a citizen science data metric for a Nature Relationship Index
This project stems from the request of new metrics for supporting monitoring and evaluation of human development that motivate transformational change. The Nature Relationship Index has been proposed to capture the relation between humans and nature (Ellis et al. 2025). The student will through databases for citizen science data develop and calculate metrics capturing engagement in biodiversity data collection, consider limitations in data coverage and reporting and relate to other human development indices.
Ellis, E.C., Malhi, Y., Ritchie, H. et al. An aspirational approach to planetary futures. Nature 642, 889–899 (2025). https://doi.org/10.1038/s41586-025-09080-1
Contact/supervisors: Ullrika Sahlin (ullrika.sahlin@cec.lu.se)
Recommended knowledge: Programming skills. Knowledge in statistical learning.
Language: English