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to perform structure-based antibody design, developing and employing machine-learning tools for predicting antibody-epitope binding and antibody developability. In silico antibody design is a long-standing
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machine learning for medical genetics Apply for this job See advertisement About the position There is a vacant position as professor II /associate professor II (20 % position) at Institute of Basic Medical
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systems or other related fields (e.g., sociology, anthropology, history, textual studies, digital humanities, design, human-computer interaction, art history, visual culture, artistic research
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interaction, art history, visual culture, artistic research). The Postdoctoral Research Fellow will be expected to devote their time to their own research agenda as well as working with a dynamic team of
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structural level, developing and employing machine-learning tools for predicting antibody-epitope binding and antibody developability. In silico antibody design is a long-standing computational and
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-throughput experimental and computational immunology combined with machine learning. The long-term aim is to conceive in-silico novel immunodiagnostics and immunotherapeutics using the disease-diagnostic
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systems or other related fields (e.g., sociology, anthropology, history, textual studies, digital humanities, design, human-computer interaction, art history, visual culture, artistic research
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techniques to perform structure-based antibody design, developing and employing machine-learning tools for predicting antibody-epitope binding and antibody developability. In silico antibody design is a long
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Experience with machine learning applications Personal skills In the evaluation of applicants, emphasis will be placed on the following personal skills: Candidate’s good co-operative skills, and the ability
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tools Excellent knowledge of programming languages such as R, Python, etc. Familiarity with AI algorithms and machine learning Excellent written and oral communication skills in English Desired