14 machine learning positions at Umeå universitet in Sweden

1-2 Postdoctor (2 years) within Optimization for Machine Learning

agreement. Apply latest 17 May 2024. Project description and working tasks The connection between optimization and machine learning is at the heart of many recent breakthroughs in artificial intelligence and

PhD student in Computing Science with focus on machine learning for continuous and discrete structures

in machine learning for continuous and discrete structures. The department has been growing rapidly in recent years. An inclusive and participatory environment are key elements in our growth. The 60

Research Engineer in AI and Machine Learning

machine learning (ML). The work involves collecting and processing data, setting up and configuring development environments, and providing services for benchmarking, i.e., standardized performance

PhD student in Computing Science with focus on data privacy

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

PhD position in mathematics or mathematical statistics, with focus on geometric deep learning

26th 2024. Project description and tasks Machine learning (‘artificial intelligence’) is having an immense impact on both society at large and research especially, and this impact is expected to increase

Postdoc (3 years) in Mathematical Modeling of Microbe Evolution

be eligible. Special reasons include absence due to illness, parental leave, appointments of trust in trade union organisations, military service, or similar circumstances, as well as clinical practice

Post doctor (2 years) position with focus on secure federated learning

systems, connected devices, and distributed applications poses several challenges in dealing with petabytes of data in diverse resource-constrained environments. Federated machine learning (FML) is a

PhD position in Mathematics focusing on geometric deep learning

of topics in modern machine learning research, including geometric deep learning, non-convex optimization problems and federated learning. In addition, this project is deeply connected with

Postdoctoral position (2 years) in pollination ecology

service, or similar circumstances, as well as clinical practice or other forms of appointment/assignment relevant to the subject area. Postdoctoral fellows who are to teach or supervise must have taken

PhD position in Computational Sciences within the national Data-Driven Life Sciences program

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