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. You will draw on ideas from Bayesian optimization and Bayesian deep learning, generative modelling, high throughput screening, and combinatorial synthetic chemistry. Responsibilities and qualifications
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expression and developability. Propose and validate optimization tools for performing (Bayesian) design of experiments. System validation and iterative refinement based on empirical data. Test and refine
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machine learning for transport simulation. A core innovation involves Bayesian metamodeling techniques to construct fast surrogate models of the simulation space, enabling efficient scenario analysis
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proven track record of research publications. Proven ability to conduct independent research Effective communication and mentoring skills. A commitment to diversity and inclusion in the workplace
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supply chain design, including the application of multi-objective optimization techniques, particularly within the context of biorefineries and biomanufacturing. The successful applicant will also bring
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organisations and a successful track record of national and international research projects. You will have a close collaboration with the Food lab at DTU Skylab regarding the product development activities
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and employ a scoring/ranking system for evaluating sensitisation capacity The overall objective of your research will be to reliably determine the sensitising potential of novel foods and food proteins