Computational Science Degree Projects at COSHE
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: Multicolor optical DNA mapping
The sequence of base pairs of DNA molecules is the genetic blueprint of any living being. Recent experimental developments in our collaborators' labs at Chalmers University allow direct sequence-specific visualisation of long pieces of DNA molecules – optical DNA maps. Such maps serve as a coarse-grained representation – fingerprint – of a DNA molecule's sequence. The experimental method utilises fluorescence microscopy and nano-scale devices. Recent experimental developments involve using multiple labelling strategies and high- throughput setups. During extraction of DNA from cells, usually the DNA molecules get fragmented into pieces of size 105-106 base pairs. As a consequence, experimental optical DNA maps are typically not complete representations of the chromosomal DNA sequence. In this project you will develop computational methods for assembling fragmented (noisy) optical DNA maps into full chromosomal maps for bacteria. You will get access to new multiple-labelling experimental data from state-of-the-art experiments in the field.
Contact/supervisor: Tobias Ambjörnsson (tobias.ambjornsson@cec.lu.se)
Recommended knowledge: Computational physics corresponding to FYTN03 and programming experience.
Language: English or Swedish.
Bionanophysics: DNA melting in forensics
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: 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: Self-adaptive ANN parameters
During training of artificial neural networks (ANNs), many ”hyper-parameters”, such as weight suppression, number of hidden nodes, choice of activation functions, etc., determine a balance between flexibility and generalisation ability. Too little flexibility will not allow the ANN to find a good solution. Too much flexibility will make the ANN over-trained on its training data set and unable to make good predictions on new data. In practical applications, hyperparameters tend to be manually set, which is time consuming and may lead to some information leaks and biased final results. In this project you will investigate algorithms that dynamically evolve hyperparameters during training, without manual interference, to see how they affect final results and computational time.
Contact/supervisor: Patrik Edén (patrik.eden@cec.lu.se)
Recommended knowledge: Neural networks corresponding to FYTN14/BERN04, some 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: 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: Dendrochronology evaluation
The aim of this project is to evaluate performance and uncertainty in common approaches for dendrochronology estimation. The student will develop an automatic workflow for dendrochronology estimation and compare the result to human aided estimation based on information from established software to evaluate the quality of crossdating and measurement accuracy of tree-ring series and methods from statistical modelling or machine learning.
Contact/supervisors: Ullrika Sahlin (ullrika.sahlin@cec.lu.se) and Johannes Edvardsson (johannes.edvardsson@geol.lu.se)
Recommended knowledge: Programming skills. Knowledge in statistical modelling and machine learning.
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.