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Field
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and machine learning to establish a modeling framework that uses omic data for providing effective degradation rates of biomolecules and predictions of their impact on soil organic matter turnover
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or equivalent and a PhD (or close to completion) in computer science, math or comparable, or an applied/life science (e. g. engineering, biology, medicine) with a focus on data analysis and/or machine learning
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training
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Deutsches Zentrum für Neurodegenerative Erkrankungen | Bonn, Nordrhein Westfalen | Germany | 28 days ago
analysis platforms integrating AI and machine learning pipelines Coding skills in Python or R for data processing and visualization is an asset Fluency in English (spoken and written) Strong scientific
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dynamics, data science, and machine learning are beneficial. What we offer: We offer a position with a competitive salary in one of Germany’s most attractive research environments. TUD is one of eleven
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a focus in economics, or related disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation
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Research Assistant (m/f/d) in the field of Theoretical Ecology and Evolution or Computational Biolog
, there is the opportunity to work on your own research topics and establish a junior research group. Your duties: Support ongoing research projects by developing computer models and performing computer-based
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, the student will collaborate with researchers who apply data assimilation and machine learning methods to the developed models. Your responsibilities: Analysing a global compilation of paleomagnetic sediment
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) with excellent grades in computer science, materials science, physics, or a related discipline Practical experience in data science, including the application of machine learning (ML) methods or large
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format for the unambiguous and machine-readable characterization of software interfaces and data models Selection and configuration of algorithms for annotating and organizing research data and software