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: Completed master studies in the field of environmental sciences, forestry, landscape ecology, remote sensing or related fields Interested in remote sensing, quantitative methods and programming Prior
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Science, Transportation Research Part B, or conferences such as SIGKDD, ITSC, CIKM) Strong programming skills in Python Strong interest in scientific research with the goal of obtaining a doctoral degree
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that are technically well-grounded and at the same time represent stakeholder preferences. The integrated Research Training Group (RTG) will provide doctoral researchers with an attractive qualification program, foster
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optimization or discrete algorithms. Profound mathematical modeling and programming skills. Experience with the design and analysis of graph algorithms or multiobjective optimization models is a plus. Very good
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of seismic methods and numerical simulations, Good PC and programming skills (e.g., with Python, MATLAB), Experience with measurement techniques and field measurements using sensor technology (ideally using
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tools (e.g., Python programming) is an advantage. You enjoy working in an international team and have good communication skills. Proficiency in spoken and written English is required. For more information
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participation in the related Graduate School training programs. Qualifications The applicants should possess: an excellent or very-good university degree in economics, business studies, agricultural sciences with
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project funded within the DFG Priority Programme “Illuminating Gene Functions in the Human Gut Microbiome” (SPP 2474) and be involved in microbiology and molecular microbiology of the gut microbiota
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning
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experiences in working with remote sensing data, climate data and programming skills (R or Python) are desired. You enjoy working in an international team and you are keen on developing a key set of research