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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
nano-structures. In this project, we will combine numerical models, experiments, and artificial intelligence (AI) to guide the design of specific DNA nanoconstructs. The primary goal is to build an AI
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related robustness issues. You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree. Specialization should be in
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. Experiments in the laboratory complement the field approach. Requirements: For our project, we are looking for a highly motivated PhD student with a university degree (master or equivalent) in earth or natural
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approach for the synthesis of organic macrocycles, cages, catenanes and polymers (CLIP-ORGANIC). Main Tasks and responsibilities: During four years, the selected candidate will design, synthetize and
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Epidemiology, your primary tasks will be to: Develop and validate novel computational tools for analyzing metagenomic data Publish peer-reviewed research articles on bioinformatic tools and their application
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Scientific writing and communication You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree. Approval and Enrolment
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with both academic and industrial partners in Denmark as well as abroad. Your primary tasks will be to: Investigate suitable thermal storage configurations for centralized (district heating) and
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potentially host the PhD student for research visits. The research group is located at the main campus of SDU, which is located in Odense, the third largest city in Denmark. Denmark is known for its high
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will work closely with members of our research group, as well as with other research groups at DTU National Food Institute. You will also be involved in teaching Bachelor and Master students within our
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-free double perovskites Your Profile: Master`s degree in theoretical or computational physics, chemistry, materials science or similar fields Familiarity with atomistic simulations, high-performance