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Department of Forest Resource Management The Department of Forest Resource Management conducts education and research in the areas of forest planning, forest remote sensing, forest inventory and
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. Analysis covers preprocessing, assembly of DNA fragments into complete genomes, imputation of missing data, filtering of bad data, prediction of the meaning of the DNA in individual cells, compression
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(e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects of machine learning such as
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inference and deployment costs (e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects
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that does not reflect local reality and thereby create unfair comparisons. Your work will include measurements of greenhouse gases (CO2, CH4, N2O) using drones and various sensors, as well as remote sensing
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look forward to receiving your application! Do you have a background in machine learning and interested in telecommunications? You have a chance to contribute to development of sensing methods for new
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or abroad. The candidate must have: very good oral and written proficiency in English. good organizational and time-management skills, a high level of accuracy, and a strong sense of responsibility
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cutting-edge methods, models and technologies in environmental science, quaternary sciences, bedrock geology, paleontology, physical geography, biodiversity and ecosystem science, remote sensing, Geographic
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, remote sensing, large datasets, or fieldwork is considered meritorious. Employment process Processing of the appointment will comply with the provisions in Chapter 5 of the Higher Education Ordinance, and
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assessment criteria Experience with programming (e.g. Python, MATLAB, Fortran or similar), numerical modelling, remote sensing, large datasets, or fieldwork is considered meritorious. Employment process