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: Design, implementation and testing of new methods and algorithms so that SIESTA can harness the compute power of the latest generation of (pre-)exascale architectures and tackle novel scientific challenges
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experience, distributed into different groups Experience in the neuroscience field, preferent within the spinal cord injury field Experience in the field of molecular biology and knowledge of cell biology
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for the candidates who accomplish all the eligibility criteria stated in the job offer will be 10 points. Threshold: 5 points. The maximum score (10 points) will be distributed as follows: 1 point for required
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platform for AI-powered cancer research. Key responsibilities include: Designing and implementing a distributed analysis infrastructure. Developing procedures for the integration of AI and data analysis
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the eligibility criteria stated in the job offer will be 10 points. Threshold: 5 points. The maximum score (10 points) will be distributed as follows: 1 point for required speciality. 2 points for required academic
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for the processing and analysis of multimodal spatial biology datasets. Design and apply advanced image analysis algorithms for cell segmentation, feature extraction, and debarcoding for lineage tracing. Statistically
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-DIGITALEMERGING)" with the functions of junior research staff of group A, subgroup A1. Specific Development of distributed fibre sensors compatible with telecommunications networks. An important part of the work
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of inclusive recruitment practices and an impactful training portfolio. VIDA will recruit a total of 12 postdoctoral researchers distributed in 2 calls to conduct interdisciplinary and intersectoral research
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of the motion dynamics of nanomotors, their internalization, tissue and cell distribution and similar aspects. Development of microfluidic setups for studying collective migration and tissue
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of star formation histories and age-metallicity distributions of selected stellar samples in the Milky Way disk and halo Mechanisms of stellar migration in the Milky Way disk Chemical evolution modelling