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, such as semi-Markov models linked to disease transmission models. The framework builds on existing models but these must be adapted to account for differences in epidemiology and disease burden between
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Aerospace Engineering, Aeronautics or a comparable degree, thorough knowledge of AI/ML methods, acoustics, and air traffic management are preferred, as well as excellent programming (Python, Java, C++, …) and
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in Python and affinity with large geospatial datasets. Interest in interdisciplinary research at the interface of geoscience, engineering, and societal impact. Good communication skills and willingness
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of water volumes and deterioration of water quality in aquifers, reservoirs and rivers. This, in turn, leads to a greater pressure on the remaining water resources and more water scarcity in different
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. Building on these insights, you will run one dimensional mixed layer models to test how different conditions regulate stratification and mixing, and compare modeled responses with observations to expose
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PhD Candidate on The Future of Mixed Methods Research /Junior Lecturer in Methodology and Statistics
are appreciated but not required. Good research skills evidenced by good grades for research methods courses. Excellent data analytical skills, as evidenced by a good command of R and/or Python Experience with
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diverse group of people from different countries and disciplines, and we welcome candidates who contribute to and enjoy this diversity. Job requirements You must be able to demonstrate: Masters degree in
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programming skills in Python/C/C++ Good oral and written skills in English Enjoys working in an international and inter-disciplinary research group To thrive as a PhD candidate, it’s crucial to have a strong
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software engineering skills in Python. Strong background and research experience in astrodynamics/orbit determination and numerical methods. Experience in usage of PRIDE data is a strong plus. Experience in
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learning video-AI models; b) assess representational alignment of bio-inspired deep learning models to the human brain. The bio-inspired models will be enriched with different temporal integration mechanisms