The Department of Physics is looking for a highly motivated Postdoctoral Researcher to join “The Organic Photonics and Electronics Group” (web: Home – OPEG umu at Umeå University, Sweden. You will be part of a focused effort for the development of light-emitting electrochemical cells (LECs) for practical applications. The LEC is a novel and functional complement to the LED and the OLED since it can:
·       deliver bright area or easily patterned light emission at low voltage,
·       be thin, lightweight and flexible,
·       be fabricated in its entirety by cost-efficient ambient-air printing,
·       be highly sustainable (free from toxic and rare compounds, energy-efficient fabrication, practical recycling).

The Morimoto Lab specializes in immunology research with a focus on evolutionary perspectives of antigen receptor diversification, leveraging non-conventional model organisms. This project aims to uncover the mechanisms of hematopoietic differentiation in the unique innate immune systems of invertebrate species, potentially involving physiological genome editing processes in the host. To achieve this, the research will employ a multidisciplinary approach, integrating conventional developmental biology with advanced techniques such as CRISPR/Cas9-based mutagenesis, light-sheet microscopy, genomics, and single-cell transcriptomics.

We are announcing an opening for a highly motivated postdoctoral researcher to join a dynamic, interdisciplinary project at the forefront of plant biology and bioengineering (for more information please visit: resydeproject.org). This two-year position (with the possibility of a one-year extension) will focus on exploring and engineering the molecular mechanisms underlying plant development, with an emphasis on symmetry-breaking processes in flowers. Symmetry breaking refers to the pivotal biological phenomenon by which initially symmetrical structures develop into complex, specialized forms—an essential process in both plant and animal development. This project leverages advanced sequencing, proteomics, genome editing, and protein engineering tools to uncover how molecular players, such as microProteins, regulate developmental pathways.

Linear algebra expressions are evaluated in an efficient and robust way by mapping them to a carefully chosen sequence of calls to optimized functions as offered by libraries such as BLAS and LAPACK. The mapping is by no means unique, and different mappings differ in terms of time, space, and accuracy. When the sizes of the matrices are unknown at compile-time, as is often the case, then the problem is further complicated by the fact that no single mapping is optimal for all combinations of matrix sizes. As a consequence, any code generated (at compile-time) to evaluate the at run-time) must consist of more than just a single sequence of function calls.

We recently received a grant from the Swedish Research Council to investigate how to effectively compile linear algebra expressions when the matrix sizes are unknown at compile-time. The project aims to address the problem using e-graphs. An e-graph is a data structure commonly used in automated theorem provers and recently popularized for optimizing compilers and many other optimization tasks. We hypothesize that an e-graph would be an effective tool for discovering and encoding a large set of alternative ways of evaluating a particular expression with unspecified matrix sizes. When a concrete expression is evaluated at run-time, thus revealing the matrix sizes, an extraction algorithm can identify an optimal evaluation scheme for that particular instance of the expression from the compiled e-graph.

Trustworthiness is a crucial consideration in modern AI decision-making systems. As a fundamental aspect of human interaction, AI trustworthiness can be improved through transparency and explainability in robotic systems. This project aims to identify factors that may undermine the trustworthiness of data and models used in human-robot interaction, and further investigates common and specific trustworthiness issues related to fairness and safety in human-robot interaction applications. An AI trustworthiness model will be developed and validated ensuring that both data and models of human interaction are robust, especially in the selected industry use cases. 

The postdoc position is linked to the research group Deep Data Mining, which focuses on fusing data science and artificial intelligence and developing AI trustworthiness (e.g., fairness, privacy) models.   The project is part of the EU project XSCAVE whose ambition lies on large scale deployment of autonomous heavy mobile machines in earthmoving, forestry and urban logistics industries. The XSCAVE consortium involves eleven partners from all over Europe.  This offers excellent opportunities for international exchange and collaboration with leading research groups and companies in the field of AI robotics, large language modelling, simulation, mobile robotics and offroad heavy equipment.

Translation is a highly regulated cellular process, and recent findings have shown thousands of mRNAs to be regulated at the level of translation in plants as well as in other organisms. We have revealed that the components of the ribosome itself participate in the selection of mRNAs to translate. How this is achieved is currently unknown. In the project we will primarily use the Ribo-Seq methodology to characterize the translational regulation on a global level. We hope to shed more light on this process using genetic and biochemical tools and by studying the interaction of mRNAs and proteins during translational regulation. 

The Department of Physics is looking for a highly motivated Postdoctoral Researcher to join “The Organic Photonics and Electronics Group” (web: https://opeg-umu.com) at Umeå University, Sweden. You will be part of a larger effort for the development of a novel electroluminescent technology, termed a light-emitting electrochemical cell (LEC). The LEC is attracting interest in both academia and by industry since it is thin, lightweight and flexible, and because it can be fabricated in its entirety by ambient-air printing and be highly sustainable. Your primary tasks will be to study the temporal evolution of the morphology of the thin-film emissive material, to establish correlations between the thin-film morphology and the device performance, and to develop design guidelines for the realization of LECs with improved emission performance. You will collaborate closely with other members of the group as well as external academic and industrial partners. For information on relevant background research, please refer to our earlier publications: doi.org/10.1002/anie.202302874 and doi.org/10.1038/s41467-021-24761-x

In modern software systems and the organizations that run them, a substantial part of day-to-day decisions with critical impact on individual humans and society at large is either fully automated or heavily relies on automatically provided decision support. While machine learning approaches become increasingly prevalent in this context, the cores of the systems’ reasoning engines typically remain ‘symbolic’ (knowledge-based). These symbolic or neuro-symbolic software systems are of high practical complexity, which makes them difficult to reason about, for example when assessing which meta-level changes lead to the intended real-world impact. Working towards solving this problem, the theme of the broader project is ‘Automating Reasoning about Automated Reasoning’. From a fundamental research perspective, this relates to the automation of meta-reasoning pertaining to general-purpose reasoning methods. Here, the intended focus is on reasoning approaches that can revise their conclusions in face of new evidence/knowledge. The plan is to cover both nascent approaches that are considered promising facilitators of the fusion of subsymbolic, symbolic, and human intelligence, as well as industry-scale reasoning systems. Instances of the former class are different variants of formal argumentation, whereas in the latter case, the focus can lie on mainstream rule languages that are deployed in large-scale IT systems.

The causes of brain tumours (gliomas) and pancreatic cancers are not well understood, and there are no specific blood tests to discover glioma nor pancreatic cancer. We believe that if we understand how tumours arise, we can also find new opportunities for early diagnosis or new targets for treatment. More information about our publications and the research group can be found here.

The project involves analysing imaging from CT scans and MRI, together with -omics data such as quantitative metabolomics performed on blood samples, and clinical information, using advanced bioinformatics. The tasks include detailed bioinformatic analyses of imaging data and blood sample -omics data from the patients that later developed glioma or pancreatic cancer. Tasks also include literature searches, compiling results and writing manuscripts. Some administrative tasks, such as convening group meetings may be included, as we usually rotate this in the group.

Non-additive measures are set functions where the additivity condition is replaced by monotonicity with respect to set inclusion. Outer measures are one of the examples of this type of measures. Non-additive measures are also known as capacities, fuzzy measures, and monotonic games. They are useful to represent interactions between elements and there are applications in mathematical economy, social choice and computer science. 

Our research group (led by Prof. Vicenç Torra) has a long experience in the field of non-additive measures, working on measures for both discrete and continuous domains. Our current research includes theoretical results on non-additive measures and integrals, numerical integration using non-additive measures, Radon-Nikodym-like derivatives, optimal transport problem, and distances for these measures (f-divergence-like and Wasserstein-like), probability distributions based on these measures. We have developed applications for modeling decisions and for data privacy, and developed tools for measure identification. 

Neurosymbolic-AI approaches, that is neural symbolic reasoning and learning, try to imitate the fundamental cognitive abilities of intelligent agents: learn from their environment, and reasoning about what has been learned. In these systems, neural networks can learn  patterns of spatial relations from the environment that allow reasoning (i.e. learning to reason). Aligning semantic models of expert knowledge to the learning occurred from observing the environment can be used to explain the learning (i.e. reasoning about the learnt). Moreover, cognitive architectures store agents’ experience in their episodic memory which can be queried so that reasoning about past events can be carried out in order to infer the future (i.e. prospection).

This postdoctoral position is funded by Wallenberg AI, Autonomous Systems and Software Program (WASP). WASP is Sweden’s largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry. 

The successful candidate will perform research within the externally funded project “Innovative statistical and machine learning methods for comparing performance and outcome in register data studies”, which aims to develop, evaluate, and implement innovative statistical methods, including modern machine learning methods, to draw inferences from register data. The proposed methods will be carefully evaluated with data from educational and health registers but the developed methods are not restricted to these applications but are generic and can be used with other large-scale registers. The successful candidate is expected to plan and perform independent research but is also expected to collaborate with researchers at the department and within other subject areas. The position may also, by agreement, include teaching at undergraduate and graduate levels in statistics up to 20% of full time.