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characterization of algal food protein ingredients, ensuring high yield and quality. The latter will be optimized from several aspects, including e.g., in vitro nutrient digestibility and volatile compound profile
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yield and quality. The latter will be optimized from several aspects, including e.g., in vitro nutrient digestibility and volatile compound profile. The work will be carried out in close collaboration
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and the Faculty of Medicine in order to optimize the conditions for preclinical and clinical translational research, research strategies and development, as well as education on both undergraduate and
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are looking for a curious and driven postdoctoral researcher to join a project focused on improving how we study and optimize medical treatments. The work centers on advancing a vessel-on-a-chip platform—a
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analyze experiments on functional characterization of leukemia niche using patient materials and animal modelling. Organize, prepare, test and optimize common lab reagents, e.g. antibodies and keep up
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ocean environments, ensure safe and sustainable operations. Our activities are centered on numerical modelling (e.g. CFD, FEA, FSI, optimization, machine learning), but also include experiments and real
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, or erroneous data, Data cleaning and generation, Development of enhanced loss functions and information-theoretic methods for optimized data analysis, Machine learning-based image segmentation of tomographic
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– protein interactions or enzyme optimization. Main responsibilities The successful candidate will use and develop methods within one, or preferably multiple, of the following categories: Sequence library
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite
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the use of crystallographic software and data processing pipelines Experience working with computation clusters and managing large datasets Proven ability to develop, maintain, and optimize scientific