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Fritz Haber Institute of the Max Planck Society, Berlin | Berlin, Berlin | Germany | about 2 months 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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by August 2025. 1. Please upload verification as an additional document in the document upload section. 2. Earned PhD in nursing or health-related field and a strong work ethic and commitment
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at NYU Abu Dhabi, candidates are expected to have a strong interest in experimental research and collaborate with applied mathematicians closely. PhD holders with a solid publication record and aims
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description The postdoctoral project is focused on development and the exploitation of machine learning tools to accelerate the analysis of microtomography data at the MAXIV synchrotron facility. MAXIV
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Full-time: 35 hours per week Fixed-term: 31st March 2026 The School of Informatics at the University of Edinburgh invites applications for 2 Post-doctoral Researcher positions in Quantum Machine
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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models
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and implement innovative image analysis methods to quantify plant characteristics. Collaborate on multidisciplinary projects involving high-throughput phenotyping platforms. Apply machine learning and
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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models
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reinforcement learning and machine vision. Experience with ROS and the ROS ecosystem Special Requirements: Applicants cannot have received their PhD more than five years prior to the date of application and must
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The candidate will have a PhD or equivalent degree in bioinformatics, biostatistics, computational biology, machine learning, or related subject areas Prior experience in large-scale data processing and