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analysis, with possible specialisations in genomic and molecular biology techniques as well as in algorithms, statistics and artificial intelligence for molecular genetics. This is based on perspective and
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Join our multidisciplinary research team to develop and apply machine learning and bioinformatic algorithms in biomedical research. This PhD project will focus on developing machine learning
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of peptide design and chemistry, computational methods (machine learning, deep learning, genetic algorithms), microbiology, synthetic biology, and related areas essential to developing novel
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an individual study plan. For a doctoral degree, the equivalent of four years of full-time doctoral education is required. The Department of Microbiology, Tumor and Cell Biology (MTC) at Karolinska Institutet
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individual with a MSc degree in computer science, mathematics, chemistry, computational biology or a related subject. The ideal candidate has familiarity with one or more of the following areas: algorithmics
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at the single-cell level, using tools from optimal transport, mathematical optimization, and machine learning. In addition to method development, the work includes applying and benchmarking algorithms on both
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algorithms for AI agents to learn, apply, and generate justifications in collaborative settings. You will be co-supervised by Davide Dell’Anna (Utrecht University) and Myrthe Tielman (Delft University
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communicating and sharing information. Your job In this project, we focus on interpretability of the communication in MARL algorithms. We aim to bring together causality and multi-agent reinforcement learning
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observed in Drosophila larvae. This interdisciplinary project combines biology, neuroscience, and computational modelling to understand how the larva’s body’s physical properties influence its motor control
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the form of a human-expert informed reward function. Second, we aim for the integration of low-energy machine learning algorithms, so that the resulting AI model can run on a variety of devices, including