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following position Postdoctoral researcher (m/f/d) in Environmental Data Science and Machine Learning for the project BoTiKI Location: Görlitz Employment scope: full-time (40 weekly working hours) / part
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Fritz Haber Institute of the Max Planck Society, Berlin | Berlin, Berlin | Germany | about 7 hours ago
skills and experience and interest in data analysis, data science, machine learning and process automation would be an advantage. Previous experience with XAS or other synchrotron-based techniques would be
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) Analysis of the experimental data, ideally connecting to our machine learning tools Presentation of scientific results on conferences and in publications Requirements PhD degree in physics or chemistry, or
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of machine learning and health sciences, with unique access to experimental and clinical data. Embedded in Munich’s thriving AI landscape, fellows benefit from world-class facilities, interdisciplinary
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science, automation science, or a related field, and convincing expertise in robotic hardware. Experience with machine learning and large language models is highly desirable. Prior experience in a biological setting
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-08187-1 Your Profile: Master and PhD in biology, genomics or bioinformatics Strong background in data science or machine learning (deep learning, statistical modeling, or large-scale data analysis a plus
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team and lead the development and application of machine learning methods to large-scale genomic data generated at IPK-Gatersleben, with a focus on the impact of genetic variation on gene regulation
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us We are TUM’s unique Pathology AI lab developing new machine learning (ML) methods for automatically analyzing digital pathology data and related medical data. Such methods include the automatic
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statistical topic modelling Experience with natural language processing Experience with statistical methods and machine learning Ability to extract and process large numbers of documents from the internet
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to develop a 3D-generative algorithm for pharmaceutical drug design by using or combining novel machine learning approaches? How would you integrate machine learning, physics-based methods in an early-stage