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for society. With the launch of VIB.AI, we are expanding our mission to harness the power of artificial intelligence for life sciences research, innovation, and impact. We are now looking for a Machine Learning
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for society. With the launch of VIB.AI, we are expanding our mission to harness the power of artificial intelligence for life sciences research, innovation, and impact. We are now looking for a Machine Learning
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
or equivalent experience in machine learning or a related quantitative field (Computer Science, Artificial Intelligence, Statistics, Mathematics, Physics, Computational Biology/Chemistry). Candidates will be
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or equivalent experience in machine learning or a related quantitative field (Computer Science, Artificial Intelligence, Statistics, Mathematics, Physics, Computational Biology/Chemistry). Candidates will be
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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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) Basic computer skills (text processing, spreadsheet, presentations) Desirable but not required Experience working with mice Basic understanding of fluorescence-based microscopy and flow cytometry Basic
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analysis. Experience with machine learning. Basic understanding of immunology. Key personal characteristics You are enthusiastic and curious about scientific research. You like learning new
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and