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for species previously limited by technical or financial constraints. This technically ambitious project sits at the forefront of evolutionary and biodiversity genomics, with the potential for field-defining
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student, you will focus on mining large Actinomycete genomic, transcriptomic and metabolomic datasets to investigate their transcriptional regulatory network. You will develop and apply the latest
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materials design, prospective life cycle assessment, and industrial innovation. This PhD opportunity is part of the Marie Skłodowska-Curie Doctoral Network UP2MEM, a European training network committed
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directions will be pursued to enhance column generation using machine learning. The first line of research focuses on improving scalability by using Graph Neural Networks to identify and eliminate non
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characterization of glycoside hydrolases, and a postdoc working on computational modelling of the same enzymes. The PhD focuses on ligand-observed NMR analyses and other relevant methods to provide insight
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or experience in strong collaborations and interdisciplinary work at the intersection between machine learning, geophysics and acoustic data modeling. A strong experience with software defined radio Automatic
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well organized, structured, self-driven and enjoy interacting and collaborating with colleagues including PhD students, postdocs, and you are expected to take part in supervision of BSc and MSc students
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at international conferences. Engage in dissemination activities and gain teaching experience. Build a strong research network through collaboration with leading experts. The position is part of the Alliance project
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technologies. Responsibilities and qualifications The PhD project will focus on the design, synthesis, and characterization of new types of metal-organic network structures, which includes the detailed
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), we are offering a fully funded PhD position focusing on providing an excellent triple-I (interdisciplinary / intersectoral / international) research and training network to Doctorial Candidates, with a