15 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Heidelberg University in Germany
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arising from a range of translational applications in the area of spatial biology. Prerequisites The candidate should: Have either a PhD degree in computer science, engineering sciences, physics
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or spatial transcriptomics, or digital pathology) Strong programming (Python / R) and analytical skills, with proficiency in bioinformatics tools, statistics and machine learning. Experience with SQL
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expertise and achieve optimal results. Your Profile A PhD in Bioinformatics, Computational Biology, or a related field. Proven experience in large-scale omics data analysis, preferably MS-based proteomics
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of methodologies, from in-depth behavioral assessments to computer vision, machine learning and neuroimaging techniques, we aim to uncover the complexites of neurodevelopmental disorders. Our
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culturing, integrating multiple automated subsystems with image-based machine learning models. Our objective is to enable robotic decision-making through machine learning, paving the way for a standardized
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its detailed analysis through Oxford Nanopore Technologies (ONT). Your role will be central in creating and applying bioinformatics and machine learning tools to analyze long-read data and decipher cap
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to generate reproducible, micrometer-scale controllable, and cost-efficient disease models by bringing together experts in molecular systems engineering, machine learning, biomedicine, and disease modeling
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to supervise PhD, Master and Dr. med (thesis as part of medical studies in Germany) students. The fellow will also have the opportunity to teach as part of the institute’s Masters and doctoral program but will
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to generate reproducible, micrometer-scale controllable, and cost-efficient disease models by bringing together experts in molecular systems engineering, machine learning, biomedicine, and disease modeling
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-scale controllable, and cost-efficient disease models by bringing together experts in physical chemistry, physics, bioengineering, molecular systems engineering, machine learning, biomedicine, and disease