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Field
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Proteomic and metabolomics analysis; Biomarker identification through the use of machine learning approaches; and Multi-omics data integration with genomics, transcriptomics and methylomics data. Job
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will work on multiple projects funded by NIH/NHGRI. The objective of the position is to develop novel statistical methods and computer software and analyze large scale biological data from biobanks
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score derivation and validation, and other relevant analyses. Develops R or Python scripts for data analysis, statistical modeling, and machine learning techniques, ensuring reproducibility and efficiency
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. Required Qualifications: Doctoral degree (PhD) conferred by start date Demonstrated experience with analysis of large health databases Training and experience in machine learning and deep learning methods
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in: Udder health and animal welfare Digital learning and employee education Big data and tech in agriculture Bilingual communication (English & Spanish a plus) This position is available now. If you're
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Fritz Haber Institute of the Max Planck Society, Berlin | Berlin, Berlin | Germany | about 1 month 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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didactic skills High written and oral expression skills Computer user skills Excellent command of English Ability to work in a team We also expect: Teaching experience / experience with e-learning Experience
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data collection approaches. Familiarity with or strong motivation to learn machine learning or advanced data analytics for pattern detection and forecasting in environmental data. Familiarity with
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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worldwide, leveraging industry-standard tools and technologies to ensure the quality and reliability of the developed prototype hardware implementation. Qualifications: PhD in Electronics/Computer Engineering