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for the learning and research tasks. Necessary qualifications: The applicant should by the date of admission to PhD studies have a second-cycle degree (e.g. MSc or equivalent) in environmental science or management
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-quality omics analyses and statistical and machine-learning based modeling, as well as gaining a deeper understanding in extracellular vesicle biology. Work duties and responsibilities The main task for a
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Programme (semesters 1-5) using various forms of active learning. As a guideline, teaching, educational management and educational development work is expected to account for approximately 70% of the time
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Documented ability to work in Python Experience with machine-learning methods for record linkage and text analysis Documented experience with machine-learning methods for image-to-text transcription
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understanding: detection of objects and relations between objects, and use of these relations to infer new knowledge (i.e. reasoning); (ii) explore object affordances, learn the consequences of the actions
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expected to: develop and lead internationally successful research; develop, lead and participate in teaching at first, second and third cycle level; primarily teach small animal surgery, in addition to other
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nutrition, such as: analysis of time series data and dynamic processes, where signals and responses evolve over time. statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and
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conferences, and room to develop your own research profile. Read more about being an employee at Lund University: https://www.lu.se/en/about-university/work-us Work duties and areas of responsibility As a
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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data and multimodal datasets combining imaging and molecular measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with