Linkoping University, Sweden invites online Application for number of Fully Funded PhD Degree at various Departments. We are providing a list of Fully Funded PhD Programs available at Linkoping University, Sweden.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: PhD Student in Automatic Control with focus on complex networks and multiagent dynamics
The research area for the position is complex networks and multi-agent dynamics. Particular emphasis is placed on opinion dynamics as well as distributed problems in coordination, optimization, and learning. The research encompasses both theoretical and computational aspects. You will develop dynamic models and apply them, for example, to analyze sociotechnological networks and to model interactions between humans and AI agents (such as LLM-based chatbots and autonomous vehicles).
As a PhD student, you devote most of your time to doctoral studies and the research projects of which you are part. Your work may also include teaching or other departmental duties, up to a maximum of 20 per cent of full-time.
Deadline : May 15, 2026.
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD student in biomedical engineering, deep learning for medical images
The position focuses on developing methods for early detection of lung cancer, through fusion of chest CT volumes and clinical variables. Within the Swedish project SCAPIS, medical images and clinical variables have been collected for more than 30,000 subjects. The goal is to use this dataset to train models for early detection of lung cancer (lung nodules), and also determine if the lung nodules are benign or malignant. Since CT volumes are of very high resolution, it is necessary to develop new methods to efficiently detect all lung nodules. By combining CT volumes and clinical variables (such as age, sex, smoking status), the goal is to make the methods even better at separating benign and malignant nodules. You will become an expert in analyzing large medical datasets, and also gain knowledge on how AI can be used in healthcare.
The position involves many different types of meetings with other PhD students, researchers, supervisors, and collaborators. You are therefore expected to be able to engage in discussions and reason about matters related to your role.
Deadline : 2026-05-15
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD student in Environmental Science
Do you want to contribute to developing groundbreaking methods for measuring greenhouse gas emissions and biodiversity at the farm level? We seek a PhD student who wants to work with drone- and satellite‑based techniques to help accelerate a sustainable climate transition in the agricultural sector.
As a PhD student you will be part of a competent, open, and welcoming research group at Linköping University. We value diversity in experiences and perspectives, and the division represents diverse background– engineers, natural and social scientists. In the research groups we work together to develop and apply innovative methods for environmental research, often in collaboration with other universities or non-academic organizations.
The VERIFARM project, funded by Formas, is an interdisciplinary research project addressing financing and investments for promoting climate transition and biodiversity in the agricultural sector. This is achieved through combining research expertise from natural and social sciences within the broad field of environmental science with professional expertise from the banking sector and from agriculture. Today, reliable, detailed data are lacking regarding the greenhouse gas emissions and biodiversity impacts related to bank lending, particularly in agriculture. Therefore, banks therefore often must rely on coarse standard values or low‑precision models, making it difficult to determine which investments actually lead to desired improvements. There is also a lack of means for farmers to demonstrate success of local efforts and they are trapped in general assessment standards that does not reflect local reality and thereby create unfair comparisons.
Your work will include measurements of greenhouse gases (CO2, CH4, N2O) using drones and various sensors, as well as remote sensing of habitat diversity in agricultural environments as a proxy for potential biodiversity. You will plan and conduct field measurements and perform data analysis in a large interdisciplinary project group, together with farmers and representatives from the banking sector. The work involves both collaboration and independent research work.
Deadline : May 5th, 2026.
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD student in Cybersecurity
We are announcing a PhD student position in Computer Science within CUGS Research School in a joint effort with Cybercampus Sweden, formally based at the division for Cybersecurity at Department of Computer and Information Science, within Linköping University.
As a PhD student, you devote most of your time to doctoral studies and the research projects of which you are part. Your work may also include teaching or other departmental duties, up to a maximum of 20 per cent of full-time.
Deadline : May 4, 2026
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD Student in Automatic Control
You will work on a project on data driven control. In recent years, data-driven control in high dimensions has penetrated many new application areas. Examples include control of autonomous vehicles based on video data, simulation-based prediction of turbulent flows and precision medicine based on gene sequencing time series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related areas, but application to dynamic systems is hampered when the number of time samples is small compared to state dimensions, while latency and stability remain central concerns. As a result, learning-based feedback in high dimensions is still carried out without a solid theoretical foundation. This leads to unpredictable behavior, which is a major bottleneck for efficient and resilient control of complex systems in engineering and medicine. Your research will bridge this gap, and it will be carried out in collaboration with Professor Anders Rantzer at the Department of Automatic Control at Lund University, and within ELLIIT (elliit.se).
Deadline : April 30, 2026.
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD position in Polymer Chemistry focusing on Wood-Derived Functional and Circular Materials
The goal of this PhD project is to develop new materials concepts based on wood and wood-derived polymers and building blocks. The work will focus on how molecular-level chemical design can be translated into new material functions.
The research will explore how chemical modification, polymer design, and interfacial chemistry can transform wood and wood-derived polymers into functional materials with properties such as high mechanical performance, transparency, processability, or recyclability.
Deadline : April 30, 2026.
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD student in Medical Science
Viral gastroenteritis, mainly caused by norovirus and rotaviruses, is a leading cause of global diarrhea. Susceptibility to these viruses is influenced by expression of histo-blood group antigens (HBGAs) in the mucosa, synthesized by FUT2, FUT3, and ABO(H) glycosyltransferases, which are also linked to various intestinal disorders as well as the efficacy of the live rotavirus vaccines.
In this project we will combine evolutionary analyses, historical data, and functional organoid studies in a multidisciplinary approach.
By performing analysis of ancient DNA databases for common variants in genes influencing HBGA expression, we will track their frequency changes in human populations over historical times. Integrating these findings with historical, geographic, and pandemic datasets will give clues about selection pressures and health impacts. These evolutionary patterns will be linked to disease associations in contemporary populations. Further functional studies using intestinal organoids, combined with analysis of viral evolution during course of infection, will be performed to understand mechanisms behind infection susceptibility, virus infectivity and disease, as well as rotavirus vaccine response.
Deadline : April 28, 2026
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD Student for Sustainable and Resource-Efficient Machine Learning
Machine learning has recently advanced through scaling model sizes, training budgets, and datasets; often at substantial computational and environmental costs. This PhD project targets sustainable and resource-efficient machine learning with a focus on methods that reduce compute, energy usage, memory and storage demands, and associated carbon emissions while aiming to maintain model quality.
Your work will include developing new methodologies and algorithms for resource-efficient learning, for example via data selection and filtering (leveraging that not all data is equally informative). You will also investigate complementary approaches that reduce inference and deployment costs (e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects of machine learning such as robustness, fairness, and accessibility.
You will design and run reproducible experiments, measure relevant resource metrics, implement prototypes in Python, and communicate results through publications and research presentations. The exact research direction will be defined jointly with your supervisors.
Deadline : April 24, 2026.
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD student in Spatio-Temporal Machine Learning
Spatio-temporal processes are everywhere in science and engineering, with applications ranging from weather prediction to cardiovascular medicine. Developing machine learning methods that can accurately model such processes remains an open and active research frontier. This PhD project is fundamentally about advancing that frontier, contributing new methods for generative modelling, data assimilation, and multi-scale neural network architectures applied to spatio-temporal data.
The development of these methods is motivated by a concrete and important application: inferring gas transport and dispersion in urban environments. This setting is both scientifically demanding and practically far-reaching, with applications related to air quality monitoring, emergency response to hazardous releases, and greenhouse gas flux estimation. Current approaches struggle to assimilate data from heterogeneous sensor networks, are too computationally demanding for real-time deployment, and lack reliable uncertainty quantification.
Addressing these shortcomings is a research challenge in both core machine learning methodology and the application domain. This project will tackle both in close collaboration with climate scientists (read more under “Your workplace” below).
Deadline : April 24, 2026.
About Linkoping University, Sweden –Official Website
Linköping University is a public university in Linköping, Sweden. Linköping University was granted full university status in 1975 and is now one of Sweden’s larger academic institutions. Education, research and PhD training are the mission of four faculties: Arts and Sciences, Educational Sciences, Medicine and Health Sciences, and the Institute of Technology. In order to facilitate interdisciplinary work, there are 12 large departments combining knowledge from several disciplines and often belonging under more than one faculty. Linköping University emphasises dialogue with the surrounding business sphere and the community at large, both in terms of research and education. In 2018, Linköping University was home to 32,000 students and 4,000 employees.
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