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both on the sequence and structural level, developing and employing machine-learning tools for predicting antibody-epitope binding. In silico antibody design is a long-standing computational and
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Fellow in machine learning available at Department for Informatics with the research group Digital Signal Processing and Image Analysis as part of Visual Intelligence , Norway’s leading research centre
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the Job related to staff position within a Research Infrastructure? No Offer Description The position is in the Digital Signal Processing and Image Analysis (DSB) research group, Section for Machine
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computational biology techniques to perform antibody design both on the sequence and structural level, developing and employing machine-learning tools for predicting antibody-epitope binding. In silico antibody
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for imaging Apply for this job See advertisement About the position Position as PhD Research Fellow in machine learning available at Department for Informatics with the research group Digital Signal Processing
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level, developing and employing machine-learning tools for predicting antibody-epitope binding. In silico antibody design is a long-standing computational and immunological problem. Improving
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illness. We have a large team working on developing technological solutions for these applications. We are seeking a computer science researcher to take an active role in developing novel machine learning
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complexity, ranging from high-performance computing, via reduced-order models to data-driven (machine-learned) representations. In particular, we are interested in the joint applicability of such models and to
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should be proficient in conducting quantitative analyses. Experience with large language models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. The
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. These experiments will be repeated for a database of events covering different sea ice types, conditions, locations, and rates of ice deformation (from docile to violent). Machine learning techniques will then be