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Python or R, and experience in the Linux environment Experience with large-scale data analysis, such as genomics or transcriptomics data Experience with a workflow management system such as Snakemake
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methods for single-cell data analysis (tools developed by the team : https://github.com/cantinilab ). Single-cell high-throughput sequencing, extracting huge amounts molecular data from a cell, is creating
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, enjoyment, and diversity go hand in hand. We offer a clear and welcoming career path for young and talented scientists with the ambition to grow. For more information about LIACS, see http://www.cs.leiden.edu
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, or related field; Solid background in machine learning, deep learning and foundation models such as Large Language Models; Strong programming skills (Python/C++); Proven interest in generative models
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
challenges. Proficiency in at least one programming language used for scientific or analytical purposes, e.g., Python. Excellent communication skills in English, both written and verbal. For more information
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for scientific or analytical purposes, e.g., Python. Excellent communication skills in English, both written and verbal. For more information about English requirements, please see: https://www.tudelft.nl
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interaction mechanisms between the individual AWE systems of the farm will be taken into account. Many components of this toolchain already exist and have been validated for smaller-scale AWE systems (https
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, scanning system, and signal detection within a multi-beam framework. For this purpose, you will use commercial simulation software tools such as EOD and GPT, while also developing custom Python-based
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-supervised tasks (i.e. k-nearest neighbours, Decision Trees, Neural Networks, Logistic Regression, Gradient Boosting, Self-Organizing Maps); Advanced knowledge of Python programming, namely numpy, pandas
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have some experience in programming (e.g., R, Python, Julia, Scala, Java, or C++) and in data analysis, management, preparation, and visualization, ideally as part of at least one end-to-end data science