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machine learning. Strong technical background, including substantial experience with programming in Python and relevant ML/statistics libraries (e.g. PyTorch, Darts, Hugging Face Transformers, Pyro, PyMC
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, statistical modelling, programming in R, Matlab, or Python. Excellent skills in written and oral English. Personal suitability and motivation for the position. The doctoral dissertation must be submitted
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and analysis, vocal recording and analysis, dataset preparation, time-series analysis, statistical modelling, programming in R, Matlab, or Python. Excellent skills in written and oral English. Personal
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recordings FELASA certificate (or equivalent personal license to conduct animal experiments) is required. Fluent oral and written English communication skills Python, Matlab, R or other relevant programming
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a strong quantitative background and extensive experience in scientific programing (using e.g., Python, Matlab, and/or Julia), as well as a strong interest in climate science and biogeochemical
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(most notably, distribution modelling); • Documented experience and skills in coding for statistical and spatial analyses by relevant tools (e.g. R, QGIS and Python), as well as ability to manage large
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analysis. Proficiency with statistical software (e.g., R, Stata, or Python) and transparent, reproducible workflows. Strong collaborative skills. Excellent written and spoken English. Desirable Experience
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related field. Documented expertise in machine learning and time-series modelling (e.g. LSTM, XGBoost, CNN). Strong programming skills in languages such as Python and R. Experience with phenotyping data
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public defence are eligible for appointment. The candidate must have a strong quantitative background and extensive experience in scientific programing (using e.g., Python, Matlab, and/or Julia), as
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background in statistics is required, as well as experience in atmospheric dynamics or climate dynamics, basic shell scripting, and python/Matlab/R or similar languages. Experience with “traditional” climate