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Field
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Social Participation). TACIT focuses on the co-design of inclusive and accessible AI and other technologies for people with disabilities through inclusive Human-Computer Interaction (HCI) and participatory
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for donor kidneys. Central to this is the use of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies
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each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies. In FlexMobility we propose a holistic approach to design
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eager to apply and valorize scientific results in this field in high-tech domains such as semiconductor machines and robots, together with highly innovative companies? Would you like to work in a team of
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unique opportunity to contribute to the technological foundations for tomorrow’s machine learning. Your job In the ERC project FoRECAST, we aim to develop theory (e.g., new probabilistic and differential
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University Medical Center Utrecht (UMC Utrecht); yesterday published | Netherlands | about 1 month ago
, epidemiologists, clinicians and lab researchers, with expertise in the field of prediction modeling, longitudinal data analysis, statistics, data science, machine learning, AI, organoid models and cystic fibrosis
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explore how artificial intelligence can be used to predict the response to therapy in cases of acute rejection of the kidney graft. Using machine-learning techniques, you will assess kidney transplant
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first line treatments for OSA, and works by delivering pressurized air to keep the airway open. Traditionally, titration of optimal PAP parameters was done in sleep labs, but home titration using auto
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at TU Delft. In this project we also work together with experimental groups at TU Delft and beyond. The Delft Bioinformatics Lab has strong algorithmic and machine learning expertise, with a profound
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theory, discrete optimization and machine learning. In this PhD position you will focus on strain-aware genome assembly, variant calling and strain abundance quantification for viruses, bacteria and yeasts