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optimization frameworks that adopt an interdisciplinary approach, integrating concepts from operations research, transport modeling, welfare economics, transport justice and machine learning. You will be based
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pharmacology. The PhD project will focus on testing and optimizing antisense oligonucleotides in preclinical in vitro models, as well as formulating these molecules into a novel biodegradable delivery system
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Job Description The project takes place in the Quantum Light Sources group at DTU Electro, where we design, model, fabricate and test sources of single photons or entangled photon pairs
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, what motivates them to engage with disruptive technologies, and how emerging regulatory frameworks and business models influence their decisions. The doctoral research will focus on mapping the diffusion
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Senior Researcher in Synthetic Biology and Metabolic Engineering of power-to-X utilizing Microorg...
. We are seeking a Senior Researcher to lead pioneering work in synthetic biology and metabolic engineering, with a particular focus on non-model prokaryotic microorganisms capable of utilizing power
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. Work will also involve electrochemical modelling using existing models and using AI based tools. The focus of the work will be to cater to the needs to high voltage/power in power electronic systems
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interventions for newborn piglets. The candidate should preferably have a veterinary background or research experience in animal models of neonatology. The successful candidate will be affiliated to the research
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microbial metabolites and its effect on chronic kidney disease and cardiovascular complications, using an in vivo model of chronic kidney disease. Responsibilities and qualifications As a PhD student, you
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data Design algorithms for correlating low-level events into process-level attack models Contribute to joint framework development with TU/e on continual learning Collaborate with industry partners
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models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train these methods in a closed-loop