If you’re a Masters degree holder and seeking Fully Funded PhD Programs, Umeå University, Sweden has several online applications open for PhD programs. Explore the PhD opportunities across diverse research areas and submit your application soon.
1. Fully Funded PhD Position in mathematics or mathematical statistics, with focus on geometric deep learning
Summary of PhD Program:
Machine learning (‘artificial intelligence’) is having an immense impact on both society at large and research especially, and this impact is expected to increase. This boom is driven by so-called deep neural networks, a class of machine learning models proven incredibly powerful, versatile, and capable of solving many machine learning tasks. Mathematicians have taken huge steps towards theoretically understanding their empirical success, but many open questions remain.
A subfield within neural network theory is geometric deep learning. It concerns symmetries in the data or the learning task and constructing neural networks that react properly to them (equivariant networks). Examples of such symmetries are symmetries towards rotations of point clouds, translations of images or permutations of nodes in graphs. Combining the geometric/algebraic theory of (group) symmetries with the more analytical/statistical theory of machine learning allows for mathematically multifaceted research.
Application Deadline: 2024-08-26
2. Fully Funded PhD Position in Experimental Physics
Summary of PhD Program:
The ‘Ultrafast Nanoscience’ group at Umeå University is looking for a highly motivated PhD candidate interested in performing research on ultrafast spectroscopy in condensed matter nanosystems. The project interfaces exciting fields of research, including ultrafast science, photonics and magnetism, with an overarching goal to understand the dynamics of electronic degrees of freedom (charges and spins) in nanostructured materials.
The PhD student will study the physical mechanisms underlying light-matter interactions in nanoscale magnetic materials. By combining time-resolved (pump-probe) spectroscopy, finite element method and micromagnetic modelling, the candidate will design and realize plasmonic excitations affecting magnetic phenomena on ultrafast timescales. The project will disclose novel mechanisms which might have a huge impact on forthcoming light-driven data processing technologies.
Application Deadline: 2024-06-30
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3. Fully Funded PhD Position in Medical Science
Summary of PhD Program:
The position aims at a doctoral degree and the doctoral student’s main task is to engage in their own doctoral training, which includes participation in research projects as well as doctoral courses, journal clubs, seminars, etc. The main goal of the project is to develop an AI system for rehabilitation of patients with impaired arm and hand function. At the hand surgery clinic in Umeå, patients with impaired function in their arm and hand are treated. This may include traumatic amputation injuries as well as congenital absence or underdevelopment of a part of the upper extremity, a condition sometimes referred to as dysmelia. Injuries to the arm’s nerve plexus (plexus injury), or other nerves, can lead to a dramatic reduction in motor functions in the arm and hand.
Application Deadline: 2024-06-30
4. Fully Funded PhD Position in Sociology
Summary of PhD Program:
The Department of Sociology will employ two PhD students in two related but different research projects: The first project is called ‘Disentangling discrimination in Europe: Patterns, processes, and consequences of discrimination experiences among multiple targeted groups in a comparative perspective’. The PhD student in this project will work with survey data (e.g. from the European Commission for Human Rights) to answer questions about both the causes and consequences of discrimination in society. Collaborations in an international environment are part of the work, as the project’s partner university is in Alicante, Spain.
Application Deadline: 2024-06-19
5. Fully Funded PhD Position in Computational Sciences within the national Data-Driven Life Sciences program
Summary of PhD Program:
This project addresses a fundamental topic in evolution concerning how and why microbes interact in the ways they do. We know that microbes have many possible options, or strategies, regarding what resources they consume to grow. Depending on these choices, pairs of microbes may either compete or cooperate. What actually happens depends on the ability of microbes to correctly assess the situation, i.e. solve a type of inference problem. Yet, little is known about the difficulty of this inference problem or what heuristics organisms might evolve.
This research project is a collaboration between Eric Libby and Laura Carroll. It involves using machine learning techniques to infer the mechanisms by which microbes make decisions, bioinformatic techniques to compare these mechanisms to empirical data, and game theory and modeling approaches to improve our understanding. The project has many exciting directions and opportunities for different quantitative tools and approaches.
Application Deadline: 2024-06-17
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6. Fully Funded PhD Position in Computing Science with focus on data privacy
Summary of PhD Program:
The project will develop privacy-aware machine learning (ML) models. We are interested in building models that are explainable and are extracted from complex and heterogeneous data. Within explainable ML, we are interested in topics as provenance, interpretable and transparent models. Within privacy, we are interested in different types of privacy measures and models (differential and integral privacy, k-anonymity), different scenarios (centralized and decentralized data; local and global privacy). For decentralized data, we consider federated learning. Other topics of interest for this project are: aggregation, voting, game theory, and graph theory.
Application Deadline: 2024-06-14
7. Fully Funded PhD Position in developing membranes for gas separation applications
Summary of PhD Program:
Our research group strives to design and synthesize innovative materials that will shape the future of separation science. In this particular project, we are dedicated to synthesizing polymer, and fabricating carbon molecular sieve membranes for gas separation application. The project represents a true intersection of various disciplines, offering you the opportunity to actively participate in the design and synthesis of polymers, fabricate membranes, and test the membrane for gas separation applications. Within this project, you will have access to cutting-edge gas separation facilities (e.g., gas booster, gas permeation, gas filtration, etc.), spinning hollow fiber membranes (single layer and dual layer), and carbonization equipment. Your contributions will play a crucial role in advancing our understanding and development of new materials for gas separation applications.
Application Deadline: 2024-06-12
8. Fully Funded PhD Position in informatics
Summary of PhD Program:
For employment as a doctoral student, admission to doctoral education is required. The basic eligibility to be admitted to postgraduate education is for those who have completed an advanced level degree in informatics or equivalent. The department’s complete eligibility for admission to our phd programme can be read here – Syllabus for the PhD programme in informatics. In order to fulfill the requirement for special eligibility, it is required that the applicant has basic university education in informatics of at least 90 higher education credits, as well as 60 higher education credits at advanced level in informatics (master’s level) or other education of equivalent scope and depth.
Application Deadline: 2024-06-12
9. Fully Funded PhD Position in Mathematics focusing on geometric deep learning
Summary of PhD Program:
Deep learning has enjoyed tremendous success on an impressive number of complex problems. However, the fundamental mathematical understanding of deep learning models is still incomplete, presenting exciting research problems spanning areas such as differential geometry, numerical analysis, and dynamical systems. Neural ordinary differential equations (NODEs) mark a recent advance in geometric deep learning, the pursuit to incorporate symmetries and non-Euclidean structures in machine learning using geometrical principles. NODEs describe the dynamics of information propagating through neural networks in the limit of infinite depth using ordinary differential equations (ODEs) on manifolds and offer several appealing properties.
Application Deadline: 2024-06-10
10. Fully Funded PhD Position in Mathematics focusing on complex geometry and optimal transport
Summary of PhD Program:
In the 80’s and 90’s a surprising phenomenon was observed by physicists: It seemed like certain complicated geometric objects (Calabi-Yau manifolds) appeared in pairs, one taking the form of a mirror image of the other. The phenomenon was dubbed mirror symmetry and similarly as for other duality principles in mathematics, for example the duality of time and frequency in Fourier analysis, researchers quickly realized it could be very useful. An important branch of research with the aim of understanding mirror symmetry is the SYZ-conjecture, which gives a detailed description of a conjectural structure in Calabi-Yau manifolds. To show that this description is true has turned out to be very difficult. In essence, the problem consists of controlling the limit of solutions to certain (Monge-Ampère) partial differential equations when the dimension of their domain drops, which is a very challenging problem in general.
Application Deadline: 2024-06-10
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11. Fully Funded PhD Position in Experimental Physics with focus on Optical Frequency Comb Spectroscopy
Summary of PhD Program:
We are now seeking a PhD student to work on the project aimed at high-resolution broadband spectroscopy of molecules of importance in astrophysics. Satellite- and ground-based observations of hot-Jupiter exoplanets revealed the presence of molecular species in their atmospheres. The observed spectra carry information about the composition, conditions and photo-chemistry in the exoplanetary atmospheres. To extract this information, accurate theoretical models of high-temperature spectra are needed. These, in turn, must be verified by data obtained from high-precision laboratory measurements. Such data are missing for many molecular species, because high-temperature spectra are difficult to obtain and very congested, and therefore hard to analyze. To circumvent this problem, we employ double-resonance spectroscopy with a frequency comb probe to selectively measure and assign individual hot-band transitions of methane and other molecules of importance in astrophysics. In double-resonance spectroscopy, a high-power laser pumps a single energy level and a weaker laser probes transitions from this selectively populated level to higher energy levels.
Application Deadline: 2024-06-10
