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or more statistical or scripting languages, preferably R or MATLAB. Knowledge of survival-based statistical analysis, such as Cox regression and Kaplan-Meier analysis. Working knowledge of machine learning
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quantitative genetics, Bayesian methods, machine learning, large-scale genomic datasets, single-cell omics or integrative omics analyses would be highly regarded if the candidate was not initially trained in
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a new computational paradigm that combines the versatility of the digital computer with the efficiency of close-to-physics computing. The group targets the full computational stack, from materials
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scalable way. This project aims to solve this by combining different kinds of data - structural, functional, and causal - into a single AI-centred computer model. We focus on the larval zebrafish, a small
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for tumor behavior and clinical outcomes Development and implementation of artificial intelligence and machine learning algorithms for biologically and clinically motivated questions in pediatric oncology
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on the theoretical quantum matter, including (but not limited to) ultracold atomic physics, atom array quantum computation, non-equilibrium quantum matter and quantum machine learning. The postdoctoral fellows
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focused on using spatial profiling and machine learning of human specimens in combination with functional experiments in animal models to understand cancer initiation, progression, and metastasis. We
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Design-Build-Test-Learn (DBTL) cycle that rapidly iterates the design, synthesis, and evaluation of molecular-robot components. https://molbot-ex.org/index.html [Work content and job description] The E01
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uncertainty. Utilize machine-learning and data-mining approaches to recommend bioengineering interventions. Develop new machine-learning algorithms. Integrate machine learning techniques with mechanistic
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the soil-water-plant-air continuum using process-based models. You will learn how to take proper soil, plant and air samples that influence carbon and nitrogen dynamics and learns how soil and plant