Uppsala 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 Uppsala University, Sweden.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in computational linguistics with a focus on criminal procedure law
The doctoral studies are conducted within the framework of the research environment “International Center for Evidence-Based Criminal Law (EB-CRIME)”, funded by the Swedish Research Council, the Knut and Alice Wallenberg Foundation and the Riksbank’s Jubilee Fund.
EB-CRIME is a constellation of 11 researchers specialized in nine different disciplines; law, psychology, sociology, information technology, genetics, medicine, chemistry, anthropology and digital forensics. The researchers collaborate in 6 sub-projects, all of which deal with commonly used types of evidence in criminal cases. The 6 sub-projects are: Oral statements, digital forensics, molecular diagnostics, forensic anthropology, forensic medicine and age assessments. The purpose of the research is to contribute to ‘best practice guidelines’ for practitioners such as forensics, coroners, police officers, prosecutors and judges.
This doctoral project will be conducted within the framework of the sub-project oral statements. Within this sub-project, e.g. whether automated language analysis or human raters are best at determining whether someone is lying or telling the truth. This includes language technology and scenario-based experiments with e.g. police officers, prosecutors and judges. In addition to this, it is evaluated which features distinguish sincere and lying statements, as well as which signals human assessors use in practice. The doctoral project primarily aims at the parts that include language technology but also collaborations with other EB-CRIME researchers regarding the comparison with human assessors etc.
Deadline : October 4. 2024
(02) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in stabilizing measures for power systems
- We work with all scales, from simpler mathematical models to understand relationships, to advanced simulations to see details, via verifying measurements in a laboratory environment to full scale where we extract data from different operating areas.
- We need to develop control engineering simulation environments that also model physical components for power systems and electricity production facilities. Analyze and adapt data from operation of different types of power plants.
- Write scientific articles with results and communicate results at conferences and seminars. Collaborate with people in the department and externally. Supervise and teach students at undergraduate and master’s level.
- Learn to project manage your work and that of others and become an independent researcher.
- The duties may also include participation in teaching and other departmental work, however a maximum of 20% of the working time.
Deadline : 31 October 2024
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(03) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in nutrition science
In Sweden, the number of food poisonings is estimated to be half a million per year, but the dark figure is large. For vulnerable groups, food poisoning can be life-threatening, causing hospitalization and death, and there is no given control point that ensures consumers’ knowledge of food safety. International research shows that vulnerable risk groups may receive inconsistent, no or incorrect information about food safety from healthcare professionals. Inadequate knowledge of food safety reduces the ability to transmit accurate information. There is a research gap precisely when it comes to nursing staff’s knowledge, attitudes and behavior in relation to food safety. The project aims to study the ability to identify and communicate food safety in different professions who are responsible for food handling at different levels within, among other things, municipal care for the elderly. The data collection will focus on risk awareness, self-efficacy and goal fulfillment in relation to food safety and to be able to predict the factors behind knowledge about pathogens and risky foods, attitudes and food safe handling. Both quantitative and qualitative data collection methods will be applied.
Deadline :December 6, 2024
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD student in Computational Materials Chemistry
We are looking for a motivated and ambitious student for theoretical studies of ion transport in solid-state battery materials. For this purpose, the main focus is to develop a multiscale computational framework for modelling ion-conductivity in complex battery materials. This framework will be used to carry out a high-throughput screening where a large number of hypothetical materials will be studied by systematically varying its composition. The large amounts of data provided by the screening will be used to decipher important connections between material structure and conductivity by using advanced data-mining strategies and machin-learning.
The main duties of doctoral students are to devote themselves to their research studies which includes participating in research projects and third cycle courses. The work duties can also include teaching and other departmental duties (no more than 20%).
Deadline : 31 October 2024
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD student in Scientific Computing focusing on Computational Inverse Problems
The focus of this PhD project is computational inverse problems, specifically ultrasound imaging. Inverse problems are the art and science of looking into a box without opening it. Mathematically speaking, in inverse problems, we reconstruct the coefficients of a partial differential equation from partial observations of a field, for instance, the acoustic pressure. In ultrasound imaging, we try to reconstruct the interior of an object from the recordings of scattered acoustic waves.
In this project, we design, analyze and implement algorithms and methods to solve inverse problems. Most imaging algorithms perform poorly in strongly scattering environments due to simplifying assumptions made in their derivation. The goal of this project is to develop imaging algorithms that overcome such limitations and apply them to 3D ultrasound imaging.
In this project, we design, analyze and implement algorithms and methods to solve inverse problems. Most imaging algorithms perform poorly in strongly scattering environments due to simplifying assumptions made in their derivation. The goal of this project is to develop imaging algorithms that overcome such limitations and apply them to 3D ultrasound imaging.
Deadline : 10 November 2024
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD position in modelling of offshore renewable energy systems with data-driven methods
Securing future electricity supply while minimizing climate and environmental impact is one of today’s foremost challenges. Offshore renewable energy systems (RES) offer significant potential to meet the electricity demand, with emerging technologies such as floating offshore wind and wave power
devices showing promise. However, there are knowledge gaps on the reliability of these technologies in the offshore environment. Non-linear dynamics can give rise to dynamical instabilities and extreme loads on critical components. These challenges are a threat not only to a secure electricity supply, but also constitute potential vulnerabilities in our society.
The PhD project will model and develop offshore renewable energy systems, with a focus on wave power and floating offshore wind. The project will develop methods to predict non-linear dynamics and vulnerabilities based on data-driven methods, computational fluid dynamics simulations and machine learning. We offer a varied and exciting work that is designed by the PhD candidates together with the research team. The PhD student will be supervised by at least two supervisors and the research group consists of several seniors and PhD students working on different aspects of renewable energy and the electric grid. The Department of Electrical Engineering also gives a salary supplement to the doctoral students at the department.
This PhD position is part of the eSSENCE graduate school in data-intensive science and is a collaboration between eSSENCE, SciLifeLab, and STandUP. The school addresses the challenge of data-intensive science both from the foundational methodological perspective and from the perspective of data-driven science applications. It is an arena where experts in computational science, data science and data engineering (systems and methodology) work closely together with researchers in (data-driven) sciences, industry and society to accelerate data-intensive scientific discovery. eSSENCE is a strategic collaborative research programme in e-science between three Swedish universities with a strong tradition of excellent e-science research: Uppsala University, Lund University and Umeå University. STandUP focuses on long-term sustainable energy supply, one of the greatest global challenges in the next decades. The groups within STandUP envision a future society that is provided with renewable, highly reliable and cost-efficient energy for all its needs.
Deadline : 15 November 2024
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD student in Materials Chemistry at the Program for Inorganic Chemistry
The PhD project involves research in the field of fundamental materials science studies of thin films of functional multicomponent materials, specifically focusing on growth using magnetron sputtering, thermoelectric properties and components, material growth of thin-film and low-dimensional materials, as well as supporting computational studies.
Deadline : 15 October 2024
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD student in Molecular Biology of Protein synthesis in Bacteria and Eukaryotes
The overall objective of the Sanyal lab is to investigate the molecular mechanisms underlying protein synthesis and protein folding to find solutions to the global antibiotic resistance problem in pathogenic bacteria, to reconstitute cellular transcription translation machinery in the test tube, to develop RNA based therapeutic tools and to study the evolution of the protein synthesis machinery. The Sanyal lab offers a stimulating multidisciplinary environment where cutting edge protein synthesis research is conducted using tools from biochemistry, biophysics, structural and molecular biology.
As part of the PhD study, the candidate will investigate the mechanisms regulating the protein synthesis machinery during dormancy and hibernation in bacteria and lower eukaryotes using rapid kinetics, FRET, and cryo-electron microscopy. The candidate will need to express, purify, and site-specifically label recombinant proteins with fluorescent probes. A major task will be to reconstruct a eukaryotic translation system in vitro using purified ribosomes, mRNA, tRNA, and translation factors. To elucidate the molecular mechanisms, the candidate will conduct kinetic experiments using stopped-flow and quench-flow techniques, and complement the results with cryo-EM in close collaboration with other members of the Sanyal lab.
Deadline : 7 October 2024
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD student in Automatic Control
In this project, the selected candidate will join us in conducting research on data-driven methods to develop mathematical models of physiological signals. There will be a strong focus on dealing with large patient cohorts and learning to extract information from collected biological data using novel methods from machine learning and system identification. The potential applications will be in cardiology. The exact details of the research project are decided in a dialogue between the doctoral student and the supervisor.
This PhD position is part of the eSSENCE graduate school in data-intensive science. The school addresses the challenge of data-intensive science both from the foundational methodological perspective and from the perspective of data-driven science applications. It is an arena where experts in computational science, data science and data engineering (systems and methodology) work closely together with researchers in (data-driven) sciences, industry and society to accelerate data-intensive scientific discovery. eSSENCE is a strategic collaborative research programme in e-science between three Swedish universities with a strong tradition of excellent e-science research: Uppsala University, Lund University and Umeå University.
The position is also supported by SciLifeLab. SciLifeLab is a leading institution and national research infrastructure with a mandate to enable cutting-edge life sciences research in Sweden, foster international collaborations, and attract and retain knowledge and talent.
Deadline : 25 October 2024
(10) PhD Degree – Fully Funded
PhD position summary/title: PhD student in in Scientific Computing focusing on Energy-Efficient Algorithmic Solutions
With the rise of exascale computing and the explosion of data-intensive applications, the demand for faster, more energy-efficient algorithms has never been greater. Fundamental kernels and solvers—key components that power large-scale simulations and data analyses—can consume a significant portion of computational resources. While parallelization has traditionally been used to accelerate computations and tackle larger problems, it often compromises certain numerical properties, presenting a complex and exciting challenge: How can we retain accuracy while pushing the boundaries of performance?
The goal is to develop next-generation, energy-efficient algorithmic solutions tailored to data-intensive applications. You will work at the intersection of numerical analysis, computer arithmetic, and algorithm design to create methods that balance speed, accuracy, and energy consumption. Concretely, the work will focus on 1) reformulating key algorithms for greater efficiency, 2) leveraging state-of-the-art computer arithmetic tools to ensure numerical accuracy, and 3) developing practical solutions using mixed- and arbitrary-precision techniques that drive down energy usage in large-scale applications.
Deadline :3 October 2024
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD student in Health Informatics
– A doctoral student will devote their time to graduate education mainly. The rest of the duties may involve teaching at the Department, including also some administration, to at most 20%.
– Conduct mixed-methods qualitative research to explore clinicians’ and patients’ experiences and opinions on the use of generative AI in healthcare.
– Independently design, execute, and analyze research studies, while also contributing to the collaborative efforts of the research team.
– Dedicated attendance at research and team meetings.
– Publish research findings in peer-reviewed journals and present them at national and international conferences.
– Engage with stakeholders, including clinicians, patients, AI developers and policymakers, to ensure the research is grounded in real-world applications and perspectives.
Deadline : 3 October 2024
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(12) PhD Degree – Fully Funded
PhD position summary/title: PhD student in in Machine Learning building on physical principles
There are two main strategies to derive and deduce models – either using theory-based first principles or data-driven approaches. This project aims to conduct basic research to create new tools for using these two modeling strategies in conjunction. Combining all prior knowledge, both in terms of available data and physical first principles, has the potential to result in better models than if we only had to rely on one of them. How physics should be combined with data-driven machine learning models depends on the problem and is an active and in many areas still underdeveloped research field.
By making machine learning models more physics-informed, they will also become more interpretable. This interpretability can transform these machine learning models from being black-box models into full-fledged scientific tools enabling new knowledge discoveries. Therefore, one aim of this project is to create machine learning models that not only can be leveraged by first principles, but in the future also can be used to enable new knowledge discoveries in the physical domains in which they are employed. Finally, the project also aims to use theories from physics to better understand why machine learning models are working, how they can be improved and quantify their fundamental limitations.
We have a strong connection with collaborators in physics and materials science at Uppsala University, with a growing interest in using machine learning methods to advance knowledge in their respective domains. These collaborations can enable relevant applications as part of the project.
The exact research topic is decided in a dialogue between the doctoral student and the supervisor. The position is funded by the Swedish Research Council.
Deadline : 18 October 2024
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD position in physics: neutron emission from fusion reactors
The goal of the PhD project is to further develop existing computer programs for calculating the expected neutron emission from a given fusion plasma (so-called synthetic diagnostics), as well as to integrate these synthetic diagnostics into the modelling framework being developed within FP3. Special focus will be on (i) implementing a more detailed calculation of the neutron emission resulting from elastic collisions between the energetic alpha particles formed in the fusion reactions and the thermal fuel ions (deuterium and tritium), (ii) validating the calculations against experimental data from JET, and (iii) using the refined computational tools to make more detailed predictions of the neutron emission at future fusion facilities, e.g. ITER and DEMO. These types of calculations form important input for the development and optimization of fusion neutron diagnostics and associated data analysis methods, which in turn play an important role for maximizing the delivered power in future fusion facilities. The work within the PhD project will take place in close collaboration with other researchers, mainly at Uppsala University, Chalmers and KTH, but also at foreign universities and research institutes.
Deadline : 2 October 2024
(14) PhD Degree – Fully Funded
PhD position summary/title: Doctoral (PhD) position in Nanotechnology and Functional materials, with focus on metal-organic frameworks (MOFs) for water harvesting and hydrogen generation
This is a project in the field of functional porous materials for applications such as gas separation (greenhouse gases), water harvesting, water purification, catalysis and drug delivery. Within the project there is expertise in the synthesis and optimization of a wide range of porous materials, including porous oxides, zeolites and metal-organic frameworks (MOFs), etc.
In this project, you are given the opportunity to focus on environmental applications of porous materials such as MOFs. Specific application areas include water harvesting by adsorption and catalytic hydrogen generation using MOFs. The project mainly involves the synthesis and characterization of existing and new MOFs, as well as the development of post-synthesis processing methods such as 3D printing and the construction of simple devices for water capture and hydrogen generation.
Deadline :1 October 2024
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD in quantum information theory
We are looking for a highly motivated and ambitious PhD student in quantum information theory, focusing especially on quantum measurement theory and its applications.
Quantum measurement theory has various non-classical features, such as incompatibility and the unavoidable measurement backaction. Recent developments and the currently ongoing second quantum revolution have shown that quantum theory can be used in cutting-edge technological applications in, e.g., communication and metrology. These applications are fueled by correlations that are stronger than those producible classically. The purpose of the PhD project is to understand the role of quantum measurements in such correlations, and to extend this knowledge to attack problems in practical quantum information tasks. The PhD project belongs to the research group led by Roope Uola, which is part of the Wallenberg Initiative on Networks and Quantum Information. The position is officially with Uppsala University, but the student is expected to spend majority of their time at Nordita in Stockholm.
The doctoral appointment is time-limited to four years of full-time studies. Holders must primarily devote themselves to their own postgraduate education, i.e. research and course work. Other duties at the institution that relate to teaching and administrative work can be included in the framework of the employment (max. 20%), which extends the employment correspondingly.
Deadline : 30 September 2024
(16) PhD Degree – Fully Funded
PhD position summary/title: Two PhD students in experimental hadron physics
We are looking for a PhD student who can participate in the collection and analysis of data from Belle II, as well as to develop modern analysis tools, possibly using machine-learning techniques, for electron-positron experiments. It is also possible to either include a theory component, or conduct a project within the hadron beam experiment HADES in Germany, if there is interest. The PhD student is expected to participate in meetings and workshops abroad work with simulations, reconstruction, analysis and interpretation of data, and contribute to tasks relevant for the operation and improvement of the experiment.
Deadline : 1 November 2024
(17) PhD Degree – Fully Funded
PhD position summary/title: PhD student in Hydrology with a focus on Coupled Processes in Crystalline Bedrock Systems
The PhD project will have a focus on developing conceptual and numerical models of coupled thermo-hydro-mechanical processes in crystalline, fractured rock masses with and specifically on applications in order to characterize, predict, and value frost damage in tunnels that are exposed to cyclic frost periods. The model will be verified against analytical solutions and validated against experimental data from field and laboratories. The overarching goal of the study is to improve the understanding of the mechanisms of frost-induced damage and splitting of rockblocks and shotcrete materials in tunnels. In addition to working at Uppsala University, the PhD candidate will have an opportunity to interact closely with the rock engineering industry and collaborate with other PhD students within Swedish Rock Engineering Research Foundation.
Deadline : 14 October 2024
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD position in Ionic Neuromorphic Devices and Circuits for next-generation computing
The human brain contains a vast number of neurons and synapses that enable unparalleled signal processing and computing functionalities. Inspired by this, neuromorphic computing (NC) mimics how the brain computes using electronic components. NC has thereby emerged as a promising technology for solving high-complexity puzzles efficiently. Nanopore/nanochannel-based ionic devices (iontronic devices), operating in an electrolyte environment, have recently emerged as competitive candidates for realising NC. They offer unique advantages, including abundant nonlinear mechanisms for mimicking neuromorphic behaviour, high biocompatibility, and low energy consumption.
In this PhD project, we will develop ionic neuromorphic devices and integrate them with traditional electronic device-based neuromorphic units to create small-scale functional modules. These circuits will emulate neural behaviour, aiming for low-power and high-efficiency computation. This project involves both experimental development/measurement and theoretical modelling/investigation. Therefore, it requires the candidate to have a solid background in physics, electronics, and mathematics, as well as strong practical experimental skills.
Deadline : 31 October 2024
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD student in Automatic Control focusing on robust statistical learning and system identification
In this project, the selected candidate will join us in conducting research in automatic control and statistical learning, developing data-driven methods to learn large-scale signals and systems from data. There will be a strong focus on developing robust methods with mathematical guarantees, focusing on dynamical, statistical, and optimization properties with potential applications to medicine. The exact details of the research project are decided in a dialogue between the doctoral student and the supervisor.
Deadline : 9 October 2024
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About Uppsala University, Sweden –Official Website
Uppsala University is a research university in Uppsala, Sweden. Founded in 1477, it is the oldest university in Sweden and all of the Nordic countries still in operation. It has ranked among the world’s 100 best universities in several high-profile international rankings during recent years. The university uses “Gratiae veritas naturae” as its motto and embraces natural sciences.
The university rose to pronounced significance during the rise of Sweden as a great power at the end of the 16th century and was then given a relative financial stability with the large donation of King Gustavus Adolphus in the early 17th century. Uppsala also has an important historical place in Swedish national culture, identity and for the Swedish establishment: in historiography, literature, politics, and music. Many aspects of Swedish academic culture in general, such as the white student cap, originated in Uppsala. It shares some peculiarities, such as the student nation system, with Lund University and the University of Helsinki.
Uppsala belongs to the Coimbra Group of European universities and to the Guild of European Research-Intensive Universities. The university has nine faculties distributed over three “disciplinary domains”. It has about 44,000 registered students and 2,300 doctoral students. It has a teaching staff of roughly 1,800 (part-time and full-time) out of a total of 6,900 employees. Twenty-eight per cent of the 716 professors at the university are women. Of its turnover of SEK 6.6 billion (approx. USD 775 million) in 2016, 29% was spent on education at Bachelor’s and Master’s level, while 70% was spent on research and research programs.
Architecturally, Uppsala University has traditionally had a strong presence in Fjärdingen, the neighbourhood around the cathedral on the western side of the River Fyris. Despite some more contemporary building developments further away from the centre, Uppsala’s historic centre continues to be dominated by the presence of the university.
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