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in mechanobiology and translational cardiovascular research would be advantageous WE OFFER: Dynamic and collaborative research team of experienced and early-career scientists Embedded in a network of
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LLM-based vulnerability detection systems. Robustness evaluation framework: you will develop comprehensive assessment methodologies to quantify the weaknesses and limitations of LLMs in software
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on mechanistic study of high cor
network led by Prof. Cecilia Persson, Uppsala Universitet. Print4Life – Advanced Research Training for Additive Manufacturing of the Biomaterials and Tissues of the Future https://cordis.europa.eu/project
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(from clinical or environmental use cases) Application of improved workflows in field studies These tasks will be carried out within the Marie Skłodowska-Curie Doctoral network METAMIC 3 - Metaproteome
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4 fully funded PhD student positions in the computational, mathematical & experimental plant science
. More than 30 research groups study regulatory networks controlling fundamental traits such as reproductive development, organ geometry and growth, innate immunity and microbiome effects on plant performance
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research environment and part of various research networks. Our research focuses on investigating single cell and population dynamics during innate immune responses. The successful applicant will join an
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on mechanistic study of biodegra
Marie Skłodowska-Curie (MSCA) doctoral network led by Prof. Cecilia Persson, Uppsala University. Print4Life – Advanced Research Training for Additive Manufacturing of the Biomaterials and Tissues
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, which allows the candidate to tailor a flexible individualized course program Training in a wide range of skills including research management, communication, networking, presentations, writing
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Center Initiative (WPI). Students benefit from cutting-edge facilities, global research networks, and a collaborative environment that fosters innovation at the intersection of disciplines. Qualifications
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specifically, the computational team will build on our previous work (PMID: 38951512) to establish and train deep neuronal network models on large existing dataset with multi-omic data. Subsequently