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study design, data analysis, manuscript preparation, presenting findings at international conferences, and mentoring students. Your competencies The ideal candidate has: A PhD in machine learning
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vegetation ecology, plant community ecology or multi-taxon biodiversity analyses analysis of field, camera-trap, trait, citizen-science, spatial or remote-sensing data GIS, spatial modelling, remote sensing
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analysis Robot control Artificial intelligence Mechanical prototyping Previous publications in major robotics journals or conferences are a plus. Furthermore, the successful candidate is expected to: Have
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Experience with synchrotron X ray scattering and/or XFEL experiments. Strong skills in crystallographic and scattering data analysis, including handling large datasets. Solid understanding of crystallography
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interaction with colleagues in the group and external partners in Denmark and abroad. You will work with the design and execution of research studies, the analysis and interpretation of data, and the
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radiotherapy. Experience with photon-counting CT will be a considerable advantage. Deep learning knowledge will be an asset. CT image analysis skills is a prerequisite. You should have a strong theoretical
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., non-invasive brain stimulation) for symptom reduction. Large-scale data analysis (e.g. machine-learning) may be involved. Training will be provided in all methodologies but prior experience with some
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electromagnetic simulation tools, such as CST Studio Suite or Ansys HFSS, and you use them systematically as part of your design workflow. You also have practical skills in numerical modelling and data analysis
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well as data analysis within the fields of protein biochemistry, structural biology, and cell biology. The candidate will also support the teaching and mentoring of other trainees in the laboratory. Further