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lentiviral vectors, and related virus-like particles (VLPs). Expertise in process characterization, critical quality attribute (CQA) assessment, and analytical validation is highly desirable. This role
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boundaries and set a course for the future – a future that you can help to shape. The Particle Physics Research group at the Department of Physics in the Faculty of Science is looking for a full-time (100
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boundaries and set a course for the future – a future that you can help to shape. The Particle Physics Research group at the Department of Physics in the Faculty of Science is looking for a full-time (100
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simulation methods and scientific software development. The successful candidate will contribute to research and development on: EMT simulation of future power systems. Simulation methods for converter
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between the archaeological (AMGC) and AI (FLAIR) teams, contributing to the development of a simulator of real-world archaeological search processes and to the AI methods that learn effective discovery
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archaeological search processes and to the AI methods that learn effective discovery strategies within it. You will combine archaeological and landscape data (maps, satellite imagery, archaeological datasets) with
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screening pipeline, from stem-cell engineering to data analysis Develop and test improvements in single-cell methods and vector design Apply the technology in steady-state and disease settings Analyse
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and adapt assimilation schemes based on generative deep learning methods (such as flow matching and diffusion models). The candidate should have previous experience in data assimilation and/or deep
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should be motivated to develop AI methods and apply them to clinically or epidemiologically meaningful research questions. We are particularly interested in candidates with experience in longitudinal data
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package contains: We are seeking a highly motivated postdoctoral researcher to work on reinforcement learning methods for public health decision-making in the context of epidemic emergencies, with potential