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experience of working with natural experiments. Skills and interests in eDNA methods and/or microscopy are seen as beneficial. Demonstrated skills to design and establish field measurements. Understanding
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how you position your own research within this context; a preliminary publication plan. Other attachments, including letters of recommendation or certificates, are not required at this point
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within this context; a preliminary publication plan. Enquiries For further information about the position please contact the project PI, Senior University Lecturer Virpi Mäkinen, [email protected]
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resource distribution. ABOUT THE POST We are looking for a doctoral researcher who will undertake a doctoral project within the framework of the wider research project. While we expect the research plan
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goals. Our other research directions of interest include privacy of AI models trained on health data as well as training AI models on health data across national borders. Key responsibilities Design
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on health data as well as training AI models on health data across national borders. Key responsibilities Design, implement and benchmark machine learning models for large-scale health datasets consisting
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. Responsibilities: Design, develop, and implement state-of-the-art foundation health models and clinical reasoning algorithms. Collaborate closely with top-tier partner universities and clinical/industrial partners
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of the wider research project. While we expect the research plan to align with the key themes of the research project, applicants are encouraged to implement approaches to the topic relevant to their own
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activities in one of the following pillars: Pretraining Foundation Health Model: Leading the design and scaling of pretraining pipelines on nationwide health databases and structured longitudinal electronic
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computing and quantum advantage is a prospective topic across four application domains: bioinformatics, breeding, biomaterial synthesis, and cellulose‑processing enzyme design. You will develop hybrid quantum