268 model-driven-development "Integreat Norwegian Centre for Knowledge driven Machine Learning" Postgraduate positions
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and screen genetically encoded peptide libraries in cancer cell models, with the aim of identifying molecules and mechanisms that influence cancer cell behaviour. The role offers an excellent
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About the role Fungal pathogens are a major threat to both plant and animal health. To better aid efforts to combat these threats, it is vitally important that we develop a better understanding of
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will also participate in and support the Center’s research and staff activities, including (but not limited to) research skill development, communications and external publications, internal events and
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Assist and develop relationships with study physicians involved in various studies Coordinates time lines on deadline driven research projects Collect all data (clinical and research) according to standard
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heat becomes one of the defining health and performance challenges of our time, HRPC aims to generate evidence-based solutions that protect people who live, train, work, and operate in hot environments
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transitions as applied to developmental contexts. The post holder must also be willing to travel to Panama to undertake fieldwork. About our Faculty As a pioneering research-driven and interdisciplinary Faculty
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recovery, using a sheep ovary model to develop clinically relevant protocols with the potential to improve fertility preservation for young cancer patients. Working alongside researchers with expertise in
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the focus will be on the mechanics of metallic kirigami. The successful application will be expected to work independently to develop Abaqus of metallic kirigami and develop reduced-order analytical models
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subparagraph e) of Article 3 of RBI-FCT, they are developed in association or cooperation between the higher education institution and one or more R&D units. I.III - Ineligible candidates: Candidates enrolled in
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of selected video anomaly-detection models, and help deliver comparative experiments across different models, thresholds and scene conditions.You will analyse false alerts and recurring failure patterns