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adapt to unexpected events. In particular, we focus on the role of faultlines—the imaginary dividing lines that split a system into subgroups—in shaping how teams collaborate and perform under pressure
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sustainability goals whilst improving operational efficiency? This PhD studentship will involve developing machine learning models, creating virtual manufacturing replicas, and implementing optimisation algorithms
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stages or within subcellular compartments. During your PhD, you will: Gain expertise in translation regulation, single-cell sequencing and developmental biology Learn to design and carry out experiments
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the team, to effectively collaborate, and to communicate in a diverse scientific environment High proficiency in spoken and written English Interest in learning effective usage of emerging computational
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(NREL), where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NREL
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challenges through innovative computational approaches and want your research to make a real difference in healthcare, we want to hear from you. Requirements Success in this project will rely on collaboration
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collaboration with the UvA data department); conducting in depth studies on a subset of UvA students to investigate the role of psychological variables (e.g., motivation) and curriculum characteristics (e.g
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learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted drug design” is led by Docent Juri Timonen
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collaborative efforts among researchers at the University of Utah and UC San Diego in developing and applying methods in predictive and causal modeling of complex biomedical and social processes and systems
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deep learning and scalable deployment Collaborate with researchers, developers, and traders to improve existing models and explore new algorithmic approaches Design and run experiments using the latest