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interdisciplinary project, combining molecular biology, immunology, genetics, and advanced bioinformatics, working between the Bateson Centre for Disease Mechanisms, School of Biosciences and the Medical School
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) that connects different countries with the overarching aim to develop cutting-edge human in vitro and in silico biomedical tools to better understand the biology of brain disease/disorders. You will employ
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CIIMAR - Interdisciplinary Center of Marine and Environmental Research - Uporto | Portugal | about 1 month ago
.Eligibility requirements: The candidate must hold a PhD in Molecular Biology, Microbiology or related areas; Demonstrated experience in nanopore sequencing (from DNA extraction to bioinformatic analysis
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, machine learning, bioinformatics, AI, or a related field. Strong background in machine learning and data analysis. Interest in single-cell omics, spatial data, or systems neuroscience. Intellectual
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biology and bioinformatics, as well as in Machine Learning (including Large Language Models). Good understanding of evolutionary and molecular biology concepts, and good statistical (data analysis) and
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germline sequestration in basidiomycetous fungi. The project will focus on the rise and fate of new mutations arising during the life cycle of different fungal species. The project will have a strong
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interested in working at the boundaries of several research domains Master's degree in computational biology, bioinformatics, systems biology, bioengineering, chemical engineering, or a related discipline
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broad range from immunology, cell and molecular biology, omics and bioinformatics. The project is funded by the Deutsche Forschungsgemeinschaft (DFG). RESPONSIBILITIES: Design and conduct experiments
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-derived neurons that generalize over different patients. To test candidate intervention strategies that normalize these phenotypes. In addition to these core activities, you will perform bioinformatic
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health, and bioinformatics. You will apply advanced AI methods - from classical machine learning to large language models and agent-based AI - on large-scale healthcare datasets, including structured