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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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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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, 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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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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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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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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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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a doctoral degree in demography, public health/epidemiology, statistical sociology, statistics, population genetics or other relevant field, have strong quantitative methods skills and an excellent
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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
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: Strong programming skills in R for data analysis and visualization, and proficient in Unix command line environments for data processing Single-cell omics Methods to study chromatin interactions such as Hi