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organoids will be plus. Dry lab: Highly motivated candidates with a PhD/MD degree in bioinformatics, genome science, systems biology, biomedical informatics, computational biology, machine learning, data
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computational materials science techniques (DFT, MD, machine learning force field modelling) with data-driven approaches. Work with team to design and implement high-throughput experimental workflows for rapid
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on resistance mechanisms in breast cancer, glioblastomas, colon cancer, and lung cancer. Advancing precision oncology through machine-learning models: We integrate multimodal patient data, including multiomic
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background in one or more of the following fields are required: Numerical solution strategies for PDEs Mathematical modelling. Furthermore, experience within machine learning, parameter estimation/inverse
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. Robert Mann (University of Waterloo), as well as a team of HDR students. The research will explore quantum aspects of physical black holes, collapse models, and the properties of ultra-compact objects
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skills and desirably some computer modelling experience, and an ability to work in a multidisciplinary team and engage confidently with partners. You will have a track record of publishing high impact
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to apply advanced AI models in areas such as catalyst design, multi-scale modeling, and spectroscopic analysis. The Research Fellow will take on a significant role in machine learning theoretical energy
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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental
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Are you keen to pioneer machine learning models that address the challenges of robot perception? We are recruiting a research fellow who will work on our EPSRC-funded research project on “Active
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a track record in computational modelling that explores the dynamics of AI systems and the development of autonomous AI agents, experience with machine learning, reinforcement learning, and generative