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analysis activities for cooperation projects and clinical analysis. Engaging on the optimization of lipidomics/oxylipin (phospho) proteome analysis methods. You hold courses independently within the scope
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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problem-solving skills and the ability to collaborate with cross-functional teams are essential. Responsibilities The main responsibilities of the role are as follows: Develop and optimize algorithms
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the provision of support to student projects. · Experience in supply chain network design and logistic optimization. · Ability to contribute to the planning and management of independent research.
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drive the gradual development of these technologies toward real-world applications. This involves engineering experimental hardware for cell culturing workflows, optimizing experimental processes, and
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prototype (MIDAS) that integrates AI-based modules with optimisation engines to support low-carbon, cost-optimal datacentre microgrid design. To manage prototyping of the software platform - overseeing build
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discrimination. You will also contribute to the implementation and optimization of machine learning and deep learning models, including DNNs, CNNs, and RNNs, enhancing the performance of our sensing system by