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AWI - Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research | Bremerhaven, Bremen | Germany | about 1 month ago
collaboration with the international network of the SwitchFloc project. Your Profile Master's Degree in Aquatic Sciences and Technology, Marine Science, Aquaculture, Biotechnology or similar; Strong analytical
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science and energy technologies Basic knowledge of artificial intelligence and data analysis methods Programming skills, ideally in Python Independent and analytical way of working Reliable and thorough
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validation, and regulatory reporter assays. Learn more about the group here . YOUR MISSION: Investigate how genetic variants affect male reproductive function Perform single-nucleus and bulk RNA sequencing
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coding skills, particularly in Python and machine learning frameworks (PyTorch or Jax) The ability for creative and analytical thinking across discipline boundaries and abstraction levels Knowledge in
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the opportunity to contribute to collaborative efforts at the interface of data science, imaging, and materials research. You will strengthen the data science and machine learning activities of the IAS-9 with
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. Qualifications The applicants should possess: An excellent or very-good university degree in economics, business studies, agricultural sciences with a focus in economics, or related disciplines Strong analytical
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disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with
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analysis and analytical data analysis workflows, together with other team members Implementing AI-based microscopy image analysis software as python packages Developing algorithms to deploy machine learning
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learning (ML) methods—including surrogate modelling, feature extraction, and inverse design algorithms Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor
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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta