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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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, or a related discipline Interested in climatology/meteorology as well as quantitative methods Prior experience in programming is a plus (e.g., using R or Python) Good communication skills and a high
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research on the effects of different forest management regimes on the protective function of forest ecosystems. The work is embedded in a collaborative project coordinated at the Technical University
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platform for early detection of cardio-metabolic diseases as well as characterization and classification of skin diseases. You will be part of our highly impactful research programs funded by European and
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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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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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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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group, a multinational insurance company. Tasks Your duties will include: Literature research Designing, implementing, and evaluating novel machine learning approaches to detect building attributes from
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information processors and how nuclei dynamics coordinate solving complex tasks. To this end you will perform fluorescence microscopy on nuclei populations and quantify their dynamics while challenging Physarum