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complex materials simulations. These agents will assist with setting up, executing, and optimizing electronic structure workflows, from standard ground-state Density Functional Theory (DFT) calculations
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refined technology available, we are getting more and more answers and further insight about the Earth and the universe surrounding us. We contribute to about 200 scientific publications a year. In the
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developing solid teaching skills. You are willing to work with problem-based learning and to involve students actively in their own learning processes through project work, supervision and dialogue-based
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/ TensorFlow / Scikit Learn Highly knowledgeable in mathematical and statistical concepts Solid foundation in mathematics for Machine Learning (Linear Algebra, Probability, Optimization). Proficient in English
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engineering, or a closely related field by the time of enrolment. The successful candidate should demonstrate the following qualifications and competences: A solid academic foundation in data analysis, machine
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-explicit ground-state models of supported and unsupported nanoparticles. Modeling catalytic reaction kinetics to identify active sites and reaction barriers, integrating experimental TEM data. Comparing
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, and use both in silico and in vitro methodology to conduct ground-breaking research? We are seeking a highly motivated PhD candidate to join the newly formed “Protein Design and Function Lab” headed by
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Are you passionate about sustainable technologies and innovative bioprocesses? Join the Bioprocess Science and Engineering group at DTU Chemical Engineering and help us develop protein-based methods
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and manipulating the electronic structures of solid-state materials, to tackle the significant and diverse challenges facing society as we transition away from technologies reliant on fossil-fuels
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of feedstock, pretreatment methods and microbial products such as, SCPs. Deploy a comparative assessment of model performance at various TRLs Tested performance difference between modes of production (solid and