30 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Simons-Foundation" positions at Leibniz
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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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, starting 01.06.2026, ending 31.05.2030): Postdoc position (f/m/d) in the department “Competencies, Personality, Learning Environments” (Focus: socio-emotional and cognitive competencies) The Leibniz
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further both professionally and personally in an interdisciplinary setting. Position DWI is looking to fill the position as soon as possible: Research Scientist Machine Learning Engineer - AI-Powered Image
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based on machine learning. Reference number 05/25 Your tasks Assessment of GaN technology in possible novel integrated GaN RF front-end configurations - Full duplex in-band transceivers - Integrated down
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machine learning algorithms Strong communication skills and ability to work in interdisciplinary teams Fluency in spoken and written English We offer: A dynamic and interactive research environment as
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: active learning (uncertain cases first), smart sampling, confidence thresholds, gradations (auto-label/review/manual), measurement and decision logic for throughput vs. quality. Proficiency in programming
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), biostatistics, machine learning, data science and research data management, and causal inference methods (Iris Pigeot, Marvin Wright, Vanessa Didelez), and etiologic and molecular epidemiology (Konrad Stopsack
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Learning, especially in spatiotemporal modelling, environmental data analysis, or multimodal learning, Practical experience in applying Machine Learning, ideally including deep learning, foundation models
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machine learning techniques or computational methods for text and data analysis is appreciated The working language of our research team is English; therefore, proficiency in English is essential. While not
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with machine learning approaches, which have revealed significant fluctuations in marine CO₂ sinks over interannual to decadal timescales — fluctuations that need to be better quantified. To advance