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from reactive to proactive. The goal is to increase transparency and trust in the DNS namespace. Key research activities will include applying machine learning and graph-based techniques to uncover
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analysis across case study regions. The successful candidate will work on the development and application of AI/Machine learning and behavioural modelling within the North and Baltic seas, utilising legacy
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, Chemistry, Physics, Engineering, Mathematics, Computer Science, Data Science, Machine Learning or Artificial Intelligence a minimum 2:1 undergraduate degree (or equivalent) Excellent spoken and written
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-Class Environment: Access to a leading research environment specializing in hardware/software for medical wearables, translational endocrinology, and machine learning for medical time-series. Cutting-Edge
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at conferences. You will work at the interface between AI, chemistry, and biology, with a proactive and interdisciplinary attitude. You will become a member of the Molecular Machine Learning team (led by Prof. F
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their pandemic potential and classification as bioweapons. This project aims to develop a machine learning-accelerated NMR platform for the discovery of high-affinity inhibitors targeting viral RNAPs. Building
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. What you should have: A 1st degree in physics or engineering. An interest in optics, some ability in computer programming A desire to learn new skills in complementary disciplines. You will work jointly
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, Germany. We Offer You: A World-Class Environment: Access to a leading research environment specializing in hardware/software for medical wearables, translational endocrinology, and machine learning
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-scale metagenomic assembly and genome recovery • Comparative genomics and molecular evolution • Machine-learning-based protein prediction • Data integration, bioinformatics and phylogenetics • Scientific
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Learning, Natural Language Processing, Human-Computer Interaction, Digital Health, Endocrinology Secondments (Preliminary Plan): UiB (Norway): 1–2 months — Patient and caregiver interviews, exploration