10 Postdoctoral Fellowship at Umea University, Sweden

As a postdoctoral fellow, you are expected to independently conduct your own research project, including publications and engagement with the surrounding community. The research will be focused on educational work with a focus on one or more of the aesthetic subjects: Visual Arts, Music and Sloyd. You are expected to contribute to the development of the department’s research environment, for example by initiating collaborations with other researchers at the department, participating in and organizing seminars and other academic gatherings. You are also expected to attend other meetings and staff activities at the department, such as workplace meetings and staff days. Teaching at the undergraduate, graduate, and doctoral level may occur up to a maximum of 20%.

The postdoc position is supported by a new Water4All Transnational Project: Monitoring river metabolism to assess ecosystem services (BREATHE). BREATHE’s broadest goal is to co-design an international sensor-based river observation system, emphasizing the use of dissolved oxygen and river metabolism as tools to quantify aquatic ecosystem services in the face of environmental change. BREATHE is made up of six international groups, with each participating country focused on advancing how aquatic sensors can be used to understand regionally-pressing environmental challenges, from water regulation, to forestry, to climate change. Included in this work will be efforts to engage stakeholders in 1) co-designing workflows that interconnect sensor data collection, integration, and modelling and 2) co-constructing case studies to demonstrate how this workflow may be applied at the local scale but also integrated internationally.

Geometric deep learning refers to the study of machine learning problems involving a-priori known symmetries. A simple example are convolutional neural networks, that are known to be invariant towards translations. Using representation theory, one can build networks that are invariant, or more generally equivariant, towards a very broad class of symmetries. These networks are mathematically highly intriguing, but have also been successfully used in several science-related applications.
Explicitly accounting for symmetry is not the standard approach in machine learning – a well set up model should be able to ‘detect’ the symmetry automatically. One way to encourage the model to respect the symmetry is to explicitly make sure that the data is symmetric – one speaks about data augmentation. 

You will be a part of the development of a larger effort aimed at the development of light-emitting electrochemical cells (LECs). The LEC is a novel technology that is attracting interest from both academia and industry since it can be thin, lightweight and flexible, since it can be highly sustainable, and because it can be fabricated in its entirety by ambient-air printing. The unique and enabling feature of the LEC is the in-situ formation of a light-emitting p-i-n junction in the active material by electrochemical doping.

Your primary task will be the investigation of the exciton dynamics in this electrochemically formed doping structure, with the goal of establishing design guidelines for the realization of LECs with improved emission performance. For these investigations you will utilize a combination of advanced spectroscopy, electrochemistry and device design and development. 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: 10.1002/adma.202310156, doi: 10.1038/s41467-025-55954-3 and DOI: 10.1002/adma.202107849

The past two decades has seen a major global focus on understanding the linkages between the aboveground and belowground components of ecosystems, and significant research effort has concentrated on understanding the feedback between plant communities, soil communities and ecosystem processes. There is also growing interest in how the linkages between these two components, and their impacts on ecosystem-level properties, drive feedbacks between the biosphere and atmosphere. Despite the profile that this topic is attracting, we still have a very limited knowledge of how aboveground-belowground linkages and their effects on ecosystems are driven by environmental context, for example about how they may vary across contrasting ecosystems, along environmental gradients, or in response to anthropogenic global change drivers.

The past two decades has seen a major global focus on understanding the linkages between plant communities and soil microbial communities and their effects on carbon cycling processes. There is also growing interest in how these linkages drive feedbacks of carbon between the biosphere and atmosphere. Despite the profile that this topic is attracting, we still have a very limited knowledge of how linkages between plant and microbial communities, or their effects on carbon cycling, are driven by environmental context, for example about how they may vary across contrasting ecosystems, along environmental gradients, or in response to anthropogenic global change drivers.

The research will aim to fill this gap by performing measurements aimed at characterizing the microbial community composition and function and its role in carbon cycling through transcriptomic- and genomic-based approaches in a large ongoing replicated field experiment on a group of lake islands in the boreal forested zone of northern Sweden that collectively represent 5000-year post fire chronosequence. The experiment was initiated in 1996 and involves continued removal of different subsets of the vegetation in different plots; it is now in its 29th year. This study system and experiment is unique globally and has resulted in several publications in leading journals including 7 in Science and Nature. The research will be generously funded through a 5-year grant from the Wallenberg Scholars award scheme.

The project will develop privacy-aware machine learning (ML) models. We are interested in models for complex data, including manifold and high-dimensional data, and language models. We are interested in both data-driven models as well as models built from synthetic data. 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.

The Privacy-aware transparency decisions research group (led by Prof. Vicenç Torra) conducts research in data privacy for data to be used for machine and statistical learning. It is well known that data can be highly sensitive, and that naive anonymization is not sufficient to avoid disclosure. Models and aggregates can also lead to disclosure as they can contain traces of the data used in their computation. We want to understand the fundamental principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose. The group collaborates with several national and international research groups, edits one of the major journals on data privacy (Transactions on Data Privacy), and has active links with the private and public sectors.

Microproteins are small proteins (<100 amino acids) encoded by short Open Reading Frames present in coding and non-coding regions. Different microproteins have been shown to play regulatory roles in diverse cellular pathways. The project aims to identify the function of newly discovered and uncharacterized microproteins in innate immune cells applying genetic engineering approaches (e.g. CRISPR) and functional screenings.

Microproteins are small proteins (<100 amino acids) encoded by short open reading frames present in coding and non-coding regions. Different microproteins have been shown to play regulatory roles in diverse cellular pathways. The project aims to identify short open reading frames with coding potential in innate immune cells and study their regulation and function. 

This postdoctoral fellow position is part of a new career program, initiated by Umeå School of Education. It is linked to the research area ethics in teacher education but aims also to strengthen educational research at Umeå University. As a postdoctoral fellow in the career program, you will be part of an interdisciplinary network in addition to your regular research context. We encourage international research exchange visits during the employment.