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science/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning
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Optimization (DPO) and reinforcement learning from human feedback, building preference datasets together with clinicians - Build and run a Red Team process with physicians, computer scientists, and patient
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statistical modelling of high-dimensional data, e.g. penalised model selection and machine learning. Demonstrable understanding of RNAseq and gene expression analysis. Experience/skills handling and securely
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of pre-eclampsia Research area and project description: Pre-eclampsia affects 1 in 10 pregnancies, yet diagnosis remains uncertain. This PhD will integrate clinical data and blood biomarkers to improve
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and quantum solid state physics is absolutely essential Good computer skills are a plus We offer Your job with impact: Become part of ETH Zurich, which not only supports your professional development
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processing, and machine learning techniques is considered an advantage. We are looking for a motivated, proactive, and curious PhD candidate that enjoys working across disciplines and contributing to a shared
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academic writing (e.g., previous conference or journal articles) Personal qualities Be highly motivated for completing a PhD Be open-minded and eager to learn Be goal-oriented, accurate, analytical and
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Master’s degree in physics, chemistry, materials science, chemical engineering, or a related field who are excited about applying machine learning and data science to real-world materials challenges
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Reflectometry The aim of the PhD project is to provide machine learning (ML) based neutron reflectometry (NR) analysis as an automatized workflow for the reflectometry instruments at the Institut Laue-Langevin
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the "Machine Learning and Gene Regulation" team led by William Ritchie, specializing in bioinformatics and post-transcriptional regulation. The scientific environment at the IGH — international seminars, journal