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capture the experiences of people with these rare diseases, particularly those who are severely affected or unable to communicate verbally. This project aims to address this gap and improve our
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. In addition, the following are requirements for the role: Strong programming and quantitative skills, particularly in Python and/or R. Experience in deep learning, machine learning, or large-scale
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student experience by supporting lead academics with classroom and lab teaching and assessment, further building the skills developed within your PhD research programme. The position is grade 6 with a
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Max Planck Institute for Chemical Energy Conversion, Mülheim an der Ruhr | M lheim an der Ruhr, Nordrhein Westfalen | Germany | 4 days ago
supervised by Prof. Dr. Serena DeBeer and Dr. Olaf Rüdiger. Your tasks The successful candidate will carry out experimental research aimed at understanding electrocatalytic heterogeneous materials in-situ and
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mammalian cell lines and primary cell cultures. Perform confocal imaging and data interpretation. Analyze experimental data using statistical software and prepare research reports. Manage laboratory resources
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, selectively forgetting, and adapting their behavior through experience, all while retaining commercial-grade mechanical properties. In this PhD project, you will synthesize and characterize semi-crystalline
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this project, enthusiastic about joining a collaborative and hard-working laboratory, and committed to the complementary experimental strategies required for its successful completion. As this project will rely
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biology / Immunology laboratory experience, such as: Flow cytometry and FACS, immunofluorescence microscopy/imaging, cell culture, tumour cell killing assays, tissue processing, etc. Molecular biology
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or equivalent Skills/Qualifications MSc degree in biotechnology, chemistry or related field. Knowledge of necessary laboratory techniques, particularly experience in cell culture. Very good written
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ingredients, a process that is traditionally slow because each substrate–strain combination behaves differently. By applying machine learning to historical experimental data, we can predict high‑potential