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the Finnish Center for Artificial Intelligence . His research group develops machine learning principles and methods focusing on a few key topics (see “Machine learning foundations” below), often
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identification algorithms that directly interface with physical hardware. We work closely with industry partners, and our research has led to several methods now used in commercial products. We are part of
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disorders) with the aim to clinically validate the methods and promote their translation to healthcare. The positions are funded from Research Council of Finland project 'VR2Real: Precision diagnostics
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, with flexibility to choose their approach and methods within the project’s overarching aims. They will prepare scientific publications and engage actively in collaboration within the project’s
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concerned with optimal transport for inverse problems. Optimal transport for inverse problems One of the central topics of the research projects is the further development of theory and methods
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will design and implement novel computer vision and machine learning methods for “sensorized” cameras that extract medically relevant features without transmitting raw video. You will evaluate algorithms
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computing in large-scale omics data analysis. Your work will focus on method development and their application to biomedical research questions. Key responsibilities include: analyzing and modeling large
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traits – and link these traits to epigenomic variation profiled via whole genome bisulfite sequencing. The candidate will also have the opportunity to explore advanced pangenome-based methods to probe
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functional properties of protein-based materials. In this position, you will develop methods for the recombinant production of post-translationally modified structural proteins, investigate their effects
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the entire population. The project utilises advanced statistical methods such as multilevel models (mixed models), fixed-effects models, cluster analysis, and sequence analysis. The selected researcher is