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to help you settle in. Discover more Find more general information about doctoral studies at Chalmers here . Application procedure The application should be written in English be attached as PDF-files, as
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. They involve high-stakes decisions with important trade-offs and uncertainties. They are also challenged by the data sampling process which gives rise to distribution shifts when comparing past and future data
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novel machine learning method development. However, you will be part of a larger cross-disciplinary research initiative involving both computer and material scientists, providing excellent opportunities
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for multiuse purposes, addressing issues on climate change adaptation and high-versus low intensity forestry. We use empirical and process based modelling, with input data from the National Forest Inventory and
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consequences of keel bone deviations: What impact do these have on hen behaviour and wellbeing? high-tech welfare assessment: Help develop a non-invasive computer vision method to track and analyze how hens move
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cross-disciplinary research initiative involving both computer and material scientists, providing excellent opportunities for practical impact by taking the outputs from the developed machine learning
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score (Academic) of 6.0 or more (with none of the sections scoring less than 5.0) TOEFL score of 550 or more (computer based test 213, internet based 79) Cambridge/Oxford - Advanced or Proficiency level
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or more (computer based test 213, internet based 79) Cambridge/Oxford - Advanced or Proficiency level Selection The selection among the eligible candidates will be based on their capacity to benefit from
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none of the sections scoring less than 5.0) TOEFL score of 550 or more (computer based test 213, internet based 79) Cambridge/Oxford - Advanced or Proficiency level Selection The selection among
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of 550 or more (computer based test 213, internet based 79) Cambridge/Oxford - Advanced or Proficiency level Selection The selection among the eligible candidates will be based on their capacity to benefit