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green transition. Due to the complexity and heterogeneity of the biological mechanisms underlying fermentation, achieving optimal performance often requires fine-grained, time-dependent, control
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devices Optimization of waveguides for photon pair sources Design and characterization of sources of entangled quantum states. SiCPIC PhD student Qualifications Candidates must have a two-year master's
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potential. Given the medicinal chemistry–driven nature of this project, we are specifically seeking a candidate with documented experience in lead optimization, including structure–activity relationship (SAR
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specifically you will be involved in assessing the dietary intake of iron and zinc in human volunteers eating either a habitual Danish diet or an optimized meat- or plant-based diet. Furthermore, you will be
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in the group of Niels Engholm Henriksen (https://www.kemi.dtu.dk/english/research/physical-chemistry ). You must have a solid foundation and interests in quantum chemistry, applied mathematics
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flexibility orchestration Scalable data and machine learning pipelines Digital twin architectures for cyber-physical energy systems AI-based energy system modeling, simulation, and optimization Secure and
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design of experiments PhD enrolment: Technische Universität Berlin DC13: Multi-Criteria Decision Making (MCDM) and Multi-Objective Optimization (MOO) to guide production of SCPs PhD enrolment: Tallinn
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-on experience in advanced enzyme technology. It is also expected that you have a strong interest in the novel type of applied enzyme technology in focus in the PhD project, which entails reaction optimizations
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relevant to the content of the ROSETTA project (https://rosetta-project.eu/ ). Applications featuring a PhD in a behavioral science–related field (e.g., marketing, consumer behavior, psychology
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, synthetic biology, yeast genetics, or related fields. Strong knowledge of heterologous protein expression and optimization. Experience with high-throughput screening and genetic-engineering technology is