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. The workplace is at BUILD at Sydhavn in Copenhagen. Your work tasks To prevent fungal growth, it is necessary to know how biobased building materials react to water and under which moisture and temperature
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Postdoc – Performance requirements for biobased construction materials used in the building envelope
of materials to provide the basis for formulating new standards using biobased materials in the building envelope. Your main responsibilities will include: Providing scenarios for moisture-related challenges and
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work at the intersection of generative AI, molecular machine learning and ML-driven experimental design. Tight wet-lab integration: You will work closely with chemists and microbiologists, seeing your
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, Electrical Engineering, Applied Physics, or related field Hands-on cleanroom experience (stepper, e-beam lithography, dry/wet etching, thin-film deposition, bonding, micro-transfer printing) Experience with
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understanding of single-cell biology, recent technologies and data science. Experience with cloud computing platforms will be an advantage Experience with wet-lab protocols RNA-seq and scRNAseq is preferred but
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-growth bioreactors. Implement, develop and improve wet lab protocols and analytical protocols for analysis of a variety of microbial processes using e.g. flow cytometry, flow cells and confocal microscopy
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generation Developing and optimizing generative models for de novo binder design Collaborating closely with wet lab scientists who will express and test the designs that have been created Developing scalable
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strong wet-lab expertise to lead the biochemical and cellular validation of these computationally designed inhibitors. Responsibilities and qualifications You will play a central role in translating
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wet-lab skills in protein handling and functional biological assays Ability to work independently and drive experimental projects It is an advantage if you have experience with: Protein–protein
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include: Developing deep learning models for spatiotemporal fusion of multi-sensor satellite data (e.g. SAR and SMAP), with soil moisture as a target variable. Designing and evaluating deep learning