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mimicked with in vivo models of metastasis, which provides unique opportunities to mechanistically dissect what drives the different cell states. You will link clinically relevant phenotypes to putative
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mimicked with in vivo models of metastasis, which provides unique opportunities to mechanistically dissect what drives the different cell states. You will link clinically relevant phenotypes to putative
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development using Python and/or other programming languages, as well as executing organic (multistep) reactions in the laboratory. We are looking for an applicant with: A Master's degree in chemistry, chemical
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Matlab, Python, Fortran or C/C++) will be beneficial. Applicants whose first language is not English require an IELTS score of 6.5 overall with a minimum of 5.5 in all sub-skills. The studentship covers
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characterizing defects such as dislocations Applying generative models (e.g., GANs, diffusion models) to augment microscopy datasets Investigating domain adaptation techniques across different imaging modalities
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other empirical damage and vulnerability data. Couple the ABM with the Regional Flood Model (RFM) to describe temporal developments of flood risk considering adaptation decisions. Different adaptation
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orders. Using 3D field recordings and environmental monitoring, you will uncover how different species behave in swarms, how they use sensory cues from their environment, bringing new hindsight about the
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Solid experience with statistical modeling, machine learning, or AI Practical skills in R and/or Python for data analysis and model development Familiarity with microbial ecology, genomics, or food safety
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, optically-pumped magnetometers, to understand how early learning develops. You will address how caregivers’ behaviour influences infants’ learning and how early differences in learning predict language and
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an exceptional international team with expertise in all aspects of the project. Your tasks will include: • Preparation of different EO and in-situ datasets for training a machine learning model • Development of ML