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will do Interpretable machine learning is a growing research area, with important applications in the biological sciences, such as understanding how different genes regulate each other within biological
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impacts of land use in ecologically sensitive regions of the world? Are motivated to explore how rearranging land use can minimize negative impacts and maximize the positive ones for different stakeholder
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the positive ones for different stakeholder groups, as a basis for policy making? Are you interested in spatial optimization algorithms and uncertainty assessment? Then this PhD position at the Department
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ingredients, a process that is traditionally slow because each substrate–strain combination behaves differently. By applying machine learning to historical experimental data, we can predict high‑potential
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explore, and how different ways of structuring learning environments influence curiosity and learning. Computational models will be used to characterise individual differences in information-seeking
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implications for both fundamental and medical sciences. Job requirements MSc degree (or nearing completion) in physics, biophysics, computational biology, or a related field. Strong programming skills (Python
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: Completed, or soon-to-be completed MSc in the biological sciences or different fields in the natural sciences (e.g. computational, mathematical, earth or marine sciences) with a strong interest in ecology and
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interactions and microbiome research; an interest in quantitative biology, phenotyping and data analysis; the ability to organise research activities effectively, to communicate research findings to different
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skills. experience in data analysis, quantitative modeling and programming (e.g., R, python); knowledge of nutrient and/or agrochemical cycles in agriculture; excellent scientific writing skills in English
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work; Experience with data analysis, such as statistics, data management, etc.; Experience in scripting/programming (e.g., R, Bash, Python); Strong interest in understanding human impacts on ecological