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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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from molecular dynamics simulation trajectories in silico. On the wet-lab side, the position involves generating both fluorescence and mass spectrometry data and optimizing protocols for its analysis
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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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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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workplace that promotes learning and development for all employees. Our team is an international and dynamic hub with great young talents from any levels and with different skill backgrounds including
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lab (marklundlab.com ), we investigate how sequence information in biological macromolecules governs recognition, binding, and dynamical structure. We combine high-throughput measurements of molecular
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identity and extracellular signaling, but how cell types, signaling mechanisms, and transcription factors jointly determine tissue structure is incompletely understood. The Koplev lab is recruiting
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experience in areas such as advanced image analysis, data structuring and data integration, and large language models (LLMs) with applications in biological and biomedical research. The successful candidate´s
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external large-scale datasets. The role focuses on establishing the data foundations for the program’s initial modelling efforts by identifying, evaluating, integrating, and structuring large biological
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biology and bioinformatics, microbiology and immunology, molecular biology, molecular biophysics, molecular evolution, molecular systems biology, and structural biology. While the foundation of our research