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science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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modeling of protein dynamics We are seeking a highly motivated PhD student to join a DDLS-funded project at the interface of structural proteomics, protein biophysics, and machine learning. The position is
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emphasis will be placed on personal characteristics such as creativity, thoroughness, a structured approach to problem-solving, and the ability to work both independently and in a team. Additional
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, thoroughness, a structured approach to problem-solving, and the ability to work both independently and as part of an interdisciplinary team. Additional qualifications Experience in simulation-based or likelihood
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. On the computational side, the project includes calculation of established and novel structural parameters that can be linked to the experimental data. The candidate is expected to be able to interpret and present
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structure – function relationships in lipid systems for drug delivery (predominantly lipid nanoparticles). The group has extensive expertise using large scale research infrastructures to study lipid systems
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these analysis methods to highly informative multimodal microscopy data and develop techniques to integrate correlated structural and molecular analysis into the natural 3D tissue space. This integration will
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possess excellent collaboration skills and a goal- and result-oriented focus. You have strong problem-solving and analytical abilities, along with an independent, structured, and meticulous work approach
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expected to actively participate in seminars and research group meetings, develop and maintain close collaboration with supervisors and colleagues, and plan, structure, and drive your work forward in