138 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" positions at Leibniz
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) Focus on Microbiome Data Science and Explainable Machine Learning Core research themes We are looking for motivated and skilled students to join our research team in the field of plant microbiome data
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At the Leibniz Institute of Plant Biochemistry in the Department of Bioorganic Chemistry a position is available for a PhD in Machine Learning for Enzyme Design (m/f/d) (Salary group E13 TV-L, part
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adjustments are required. The ultimate goal of this master’s thesis is to find a more robust solution based on machine learning (ML). Reference number 10/26 Your tasks Analyze white-light reflectance (WLR
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. or Diploma in bioinformatics or a comparable qualification Extensive programming experience Practical experience in machine learning and the application of large language models Knowledge of OMICS and image
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related field Solid understanding of machine learning, especially deep learning and transformer models Practical experience with Python and ML frameworks (e.g., PyTorch, HuggingFace, NumPy, sklearn) Basic
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experimental data. Develop computational frameworks for integrating spatial and bulk multi-omics datasets. Create and apply statistical and machine learning models for feature extraction, data harmonisation, and
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your online-application as one single pdf-file (http://www.ipk-gatersleben.de/en/job-offers/) by 25.05.2026. If you have questions or require more information, please contact Kerstin Schweigert (jobs
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with disabilities. Qualified applicants with a disability will be given preference. Your application: We are looking forward to receiving your online-application (http://www.ipk-gatersleben.de/en/job
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accordance with European and German legal regulations. Further information on data protection and the processing of personal data can be found at: https://www.isas.de/en/datenschutz . The closing date
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at: https://www.isas.de/en/datenschutz . The closing date for applications is May 16, 2026. Please apply via our applicant portal . If you have any questions (reference number 396_2026), feel free to contact