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. The successful candidate will work on cutting-edge projects involving artificial intelligence (AI) and computational pathology, with a particular focus on developing and applying machine learning algorithms
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organisations, military 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
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these datasets to detect chromosomal abnormalities and study their breakpoints. Using statistical methods and machine learning, we will explore how these structural variants arise and which recurring structures
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and is part of the National Program for Data-driven Life Science (DDLS ), generously funded by the Knut and Alice Wallenberg Foundation. Our research is focused on the use machine learning + AI tools as
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on the use machine learning + AI tools as well as more classical but largest-scale equation-based mechanistic computational models together with patient data to create clinically predictive computational
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these questions, we will determine RNA structures in vivo using cutting-edge transcriptome-wide RNA structure probing techniques that together with computational models and machine learning algorithms will generate
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Research expertise and background in data management and ML, knowledge graphs, graph neural networks and machine learning. Knowledge and experience in working with medical or clinical data is a plus Ability
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parental leave, sick leave or military service. The following experience will strengthen your application: Experimental atomic physics Optics Photonics Optomechanics Nanofabrication Nanomechanics Cryogenics
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the development of single-cell models, machine learning approaches based on cultivation data, and the integration of metabolic models with computational fluid dynamics of bioreactors. While our team consists
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-constrained models. Currently, we are advancing the development of single-cell models, machine learning approaches based on cultivation data, and the integration of metabolic models with computational fluid