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to work independently and collaboratively within interdisciplinary teams. Background in cybersecurity, machine learning, AI, or large language models (LLM) is advantageous but not a requirement
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including postdocs, PhD, MSc and undergraduatestudents. Our research focuses on thegene regulatory networks that control the development and plasticity of fat cells. Furthermore, we are investigating
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fibres and their properties and advanced machine learning techniques with your own expertise on communication systems. You are expected to take a lead role in teaching at the BEng, BSc, MSc and PhD levels
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portfolio including documentation of teaching experience Academic Diplomas (MSc/PhD) You can learn more about the recruitment process here . Applications received after the deadline will not be considered
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crystal handling, SPR machines, and two libraries of fragments (small molecules <300 Da) tailored for SPR and X-ray crystallography. This setup enables a full workflow for fragment-based drug discovery
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publication record relative to their career stage and a clear interest in interdisciplinary collaboration. Ideally, you also bring experience with machine-learning or hybrid modelling approaches, as
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publication record relative to their career stage and a clear interest in interdisciplinary collaboration. Ideally, you also bring experience with machine-learning or hybrid modelling approaches, as
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identification, and who have significant experience in applying Machine Learning (ML) and Artificial Intelligence (AI) to these areas. Applicants with theoretical, numerical, experimental, or combined research
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the interplay between qualitative and quantitative methods and data. There is a growing focus on novel computational methods such as NLP, machine learning, and AI within the group. Teaching activities in
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with troubleshooting their machines and support their understanding of core concepts. Guide students in working on their own project. Demonstrate best practices and foster the development