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of architected interfaces. The PDRA holding this position will work closely with members of MEGA Slab and will assist with supervision of MSc and PhD students. The PDRA will contribute to the project
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performance analysis, graph-driven deep neural networks, data-efficient machine learning, self-supervised learning, reinforcement learning, online learning, and meta-learning with applications
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duration of 2 years with a possibility to extend up to 5 years upon the performance and funding availability. The successful candidate is expected to theoretical and experimental research on meta-MAC layer
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Qualifications: Advanced degree (MSc or PhD) in a quantitative discipline. Experience contributing to or maintaining open-source projects. Demonstrated ability to design and build ML pipelines and APIs
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on machine learning, and developing and applying simulation methods and models for equilibrium and nonequilibrium molecular dynamics simulations. You will model meta-lactamases enzymes involved in resistance
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profile PhD in an environment-related field followed by experiences as PostDoc in related interdisciplinary research contexts, optimally with research that aimed to work on environmental evidence synthesis
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methods based on machine learning, and developing and applying simulation methods and models for equilibrium and nonequilibrium molecular dynamics simulations. You will model meta-lactamases enzymes
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successful in this role you will have: A Masters or PhD in a related discipline, and relevant experience in the fields of systematic reviews and meta-analyses, especially in the fields of PTSD and grief
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their research through investigations at national, regional, or local scales - preferably in the vulnerable regions such as the Global South. Specifically, synthesis approaches such as meta-analytic tools
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also expected to demonstrate the ability to ground their research in national, regional, or local contexts. Strong emphasis will be placed on synthesis approaches, such as meta-analytic techniques and