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Field
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optimization Develop capsid modifications to enhance vector efficiency and reduce dosage requirements Analyze complex datasets from gene therapy experiments and interpret results Collaborate with
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:00 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a
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Application Deadline 1 Dec 2025 - 12:00 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to
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in their decisions and businesses in their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? You will join a team of researchers and
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cell biology, and neurodegeneration. Contribute to the setup and optimization of novel xenotransplantation models. Collaborate across VIB (GIBA and MIND labs, amongst others) and international partners
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 10 days ago
-funded position. The selected candidate will collaborate with experts in diabetes research on two clinical trials aiming to optimize the implementation of evidence-based diabetes technologies among older
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Researcher (R3) Country United Arab Emirates Application Deadline 22 Nov 2025 - 00:00 (UTC) Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not
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Deadline 12 Dec 2025 - 22:59 (UTC) Type of Contract Temporary Job Status Not Applicable Hours Per Week 38.0 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the
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Application Deadline 25 Nov 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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simulations and multiscale spatial-omics data. • Integrate uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments