44 parallel-computing-numerical-methods Postdoctoral positions at Technical University of Denmark
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engineering. We aim to unravel the logic of genome organisation and metabolic control—with the bold vision of building synthetic life. In this role, you will develop and apply computational methods to analyse
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commercial activities. The PhD position is based in the Section for Bioinformatics, which focuses on developing and applying computational methods to solve complex problems in genomics, systems biology, and
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to computational tools like RFdiffusion, ProteinMPNN, or AlphaFold. Experience with mammalian cell culture in two and/or three dimension Ability to perform biochemical and imaging methods Strong analytical skills
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such as mechanical exfoliation and stacking, as well as characterization methods including AFM, SEM, and optical microscopy. Experience with advanced nanofabrication techniques such as e-beam lithography
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to mucosal dysfunction. Mucins remain underexplored despite their significant role in numerous health conditions, making this a high-risk, high-reward research opportunity. You will explore novel therapeutic
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modern construct design and protein engineering tools, including PCR and molecular cloning Expertise in recombinant protein expression and purification using analytical and preparative methods (e.g. FPLC
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will be to: Model the optical, mechanical and optoelectronic properties of the fibers using ray tracing and finite element method models; Select and characterize soft materials for the fibers, and use
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the experiments. You will also have the opportunity to carry out your own simulations with our numerical model. Qualified applicants must have: A strong drive to move the frontiers of science. Ample experience with
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challenges, particularly in low- and middle-income countries (LMICs) where surveillance systems are often limited or lacking. Traditional laboratory-based methods, widely used in high-income countries
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optimal alternatives that may be relevant for the decision maker. You will be responsible for developing new, fast solution methods for stochastic optimization problem that can find many near-optimal