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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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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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(or equivalent) in Computer Science, Machine Learning, Mathematics, or a related technical field. For Postdoctoral Fellows: A completed PhD in one of the fields mentioned above and a strong publication record
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the chips and demonstrate the capabilities of the PICs. The PhD will collaborate with researchers in machine learning for analysis of the recorded Raman spectra and with biologists on the utility
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physics data analysis, machine learning, and interactive and collaborative systems. The prospective PhD candidates will work in close cooperation with our current PhD students within the PhD programme, and
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the chips and demonstrate the capabilities of the PICs. The PhD will collaborate with researchers in machine learning for analysis of the recorded Raman spectra and with biologists on the utility
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technologies, grid computing and physics data analysis, machine learning, and interactive and collaborative systems. The PhD research fellow will be part of the HVL Robotics Lab , a research and innovation
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expertise in the following areas: Machine Learning in general, with an emphasis on deep learning and language modeling Model benchmarking and evaluation pipelines for NLP/LLMs Domain-aware application of AI
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human immunology and/or cellular signalling studies Experience with proteomics or other omics data analysis is an advantage. Experience with mammalian cell culture work is an advantage. Fluent oral and
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competence in seismic data analysis is a requirement. Experience from or competence in computer programming (MATLAB, Python) is an advantage. Applicants must be able to work independently and in a structured