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expect a successful candidate to have a PhD degree from a relevant field with skills and experience in computational genomics and machine learning. Familiarity with the above-mentioned data types is an
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candidate to have a PhD degree from a relevant field with skills and experience in image analysis and machine learning. Familiarity with the volumetric microscopy image data and statistical methods
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learning and/or atmospheric chemistry2. good publication records and/or project experiments on related topicsWhat we provide:1. full PhD salary2. joint supervision from both the University of Helsinki and
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the findings to clinical data using machine learning. The postdoctoral fellow will perform studies on the metabolism of healthy intestinal cells and tumour initiating cells using state-of-the-art mouse models
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or quantum computing, willingness to learn more about the other. Experience in the fields of theoretical computer science, computer engineering and programming skills are beneficial. Qualification requirements
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and associate the findings to clinical data using machine learning. The postdoctoral fellow will perform studies on the metabolism of healthy intestinal cells and tumour initiating cells using state
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of hydrogen-induced embrittlement in high-strength steels. Different modelling (phase field modelling/artificial intelligence and machine learning approach) and experimental techniques could be utilized to get
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of music research: embodiment, interaction, emotions, well-being, and self-regulation. Methodological approaches focus on experimental research, signal processing, machine learning, psychometrics, and
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human modeling, Machine Learning, Statistics. Supervisors: Matti Vihola , Juha Karvanen , Jenni Raitoharju , Ilkka Pölönen , Kaisa Miettinen , Samuli Pekkola , Mikko Salo , Sara Taskinen , Klaus
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mitochondrial function and dysfunction. An ensemble of multi-scale computational approaches (molecular dynamics simulations, quantum chemistry, machine learning, etc) are applied to study the mechanistic aspects