134 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "NORTHUMBRIA UNIVERSITY" positions at Leibniz in Germany
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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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) 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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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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months. The candidate will mainly work for the Kiel Institute’s high profile “Ukraine Support Tracker” project, which measures military, financial and humanitarian aid to Ukraine since Russia’s full-scale
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mechanisms of learning, memory formation, perception, and behavior. Researchers with a proven track record in neuroengineering and related fields — including neuro-inspired hardware, brain-machine interfaces
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accessibility by public transport or car (including free parking) 30 days of vacation Participation in the benefits program for employees („Corporate Benefits“) The BIO-MICRO project Please find a description of
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the backdrop of intensifying global geopolitical rivalry in technologies, access to resources, or financial infrastructures and military conflicts, peripheral areas in the Global East are once again being viewed
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. The positions focus on applied machine learning methods for real-world systems. Possible research directions include: Transfer learning and domain adaptation across heterogeneous production environments (e.g