Mathematical Researcher in Geometric Deep Learning

Project OverviewThe project aims to deepen the mathematical theory of geometric deep learning. This involves developing new ways of constructing equivariant networks, describing the resulting models mathematically, and analyzing how symmetries affect the training of neural networks.Key ResponsibilitiesConduct research in geometric deep learning and its...

Project OverviewThe project aims to deepen the mathematical theory of geometric deep learning, a subfield within neural network theory that concerns symmetries in data or learning tasks and constructing neural networks that react properly to them.Research QuestionsDevelop new ways of constructing equivariant networksDescribe the resulting models...

Project OverviewThe project aims to deepen the mathematical theory of geometric deep learning, a subfield of neural network theory that concerns symmetries in data or learning tasks and constructing neural networks that react properly to them.Key Research QuestionsDevelopment of new ways of constructing equivariant networksDescription of the resulting models...

Project Overview:Geometric deep learning is a rapidly growing field that combines the power of neural networks with the mathematical beauty of symmetry. As a PhD student in this project, you will delve into the theoretical foundations of equivariant networks, exploring how symmetries can be leveraged to improve the performance of deep learning...

Project OverviewWe are seeking a highly motivated and skilled PhD student to join our team at Umeå University in the field of deep-learning conifer genomics. The successful candidate will be involved in the development of novel deep-learning driven computational tools for efficient identification of transposable elements in large plant genomes.Research...

Project OverviewAdvances in high-throughput DNA sequencing technologies have led to the production of large genome assemblies, including those of conifers, wheat, axolotl salamanders, and giant lungfish. Genome annotation, particularly the identification of transposable elements (TEs), presents a significant challenge in analyzing these large genomes. Deep...

Project OverviewAs part of our ongoing research in plant genomics, we are seeking a highly motivated PhD student to join our team at Umeå University. The successful candidate will work on developing deep-learning driven computational tools for efficient identification of transposable elements (TEs) in large plant genomes.Research ObjectivesDevelop new...

Project OverviewAs a PhD student in plant science, you will contribute to the development of deep-learning driven computational tools for efficient identification of transposable elements in large plant genomes. This project aims to advance our understanding of the complexities of transposable elements in plant genomes and their impact on genome...

Project OverviewThe project aims to develop privacy-aware machine learning models that are explainable and extracted from complex and heterogeneous data. We are interested in building models that provide transparency and interpretability, while ensuring the privacy of sensitive information.Research GroupThe research group is focused on developing innovative...

Project OverviewThe project aims to develop privacy-aware machine learning models that are explainable and extracted from complex and heterogeneous data. We are interested in building models that provide transparency and interpretability, as well as different types of privacy measures and models.Research GroupThe research group is part of the Computing...

Project OverviewThe project focuses on plant genomics and the development of deep-learning driven computational tools. The goal is to develop new deep-learning driven transposable elements (TEs) identification tools to further decrease the computational demand for large genome analysis.Key ObjectivesDevelop a comprehensive TE dataset to capture the sequence...

Project OverviewThe project aims to develop privacy-aware machine learning models that are explainable and extracted from complex and heterogeneous data. We are interested in building models that provide provenance, interpretable, and transparent results. Within explainable machine learning, we focus on topics such as provenance, interpretable, and...

The project focuses on developing privacy-aware machine learning models that are explainable and extracted from complex data. We are interested in building models that are transparent and interpretable. Topics of interest include provenance, differential and integral privacy, k-anonymity, and federated learning.Research GroupThe research group is responsible...

About the ProjectThe project aims to develop privacy-aware machine learning models that are explainable and extracted from complex and heterogeneous data. We are interested in building models that provide provenance, interpretability, and transparency. Within explainable machine learning, we focus on topics such as:Provenance: understanding the origin and...

Project OverviewThe project aims to develop privacy-aware machine learning models that are explainable and extracted from complex and heterogeneous data. We are interested in building models that provide provenance, interpretability, and transparency. Within explainable machine learning, we are interested in topics such as provenance, interpretable, and...

Project OverviewHigh-throughput DNA sequencing technologies have led to the production of large genome assemblies, including those of conifers, wheat, axolotl salamanders, and giant lungfish. Genome annotation, particularly the identification of transposable elements (TEs), presents a significant challenge in analyzing these large genomes. Deep-learning...

About the PositionThe Department of Computing Science at Umeå University is seeking a highly skilled Research Engineer to contribute to the Wallenberg's research arena for Media and Language. This is a unique opportunity to join a dynamic institution with a strong focus on research and education in computer science.Main ResponsibilitiesDesign, implement,...

The Department of Computing Science at Umeå University is seeking a research engineer to contribute to the Wallenberg's research arena for Media and Language. The position involves setting up and maintaining a distributed environment for collaborative projects in machine learning.About the PositionThe main tasks of the advertised position include collecting...

Research Opportunity: Predictive Modeling for Lifelong LearningWe are seeking a highly motivated Postdoctoral Researcher to join our team in the Department of Statistics at Umeå University. The successful candidate will contribute to the development of predictive models for lifelong learning, leveraging existing prospective cohort studies and intervention...

About the PositionThe Department of Computing Science at Umeå University is seeking a research engineer to contribute to the Wallenberg's research arena for Media and Language. This is a special fixed-term contract of 12 months starting September 1, 2024 or as agreed.Main ResponsibilitiesThe main responsibilities of the announced position are to:Contribute...