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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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of political science and 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
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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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models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. Alongside developing own research ideas, applicants should be capable of turning those ideas
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models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. The evaluation of applicants primarily hinges on their documented academic qualifications and
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on how collaborative practices evolve with these powerful tools, and support learning in disciplinary or interdisciplinary contexts. In addition, the nature of the interaction between human and machine
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SUMOylation, transcription factors, or chromatin dynamics. Expertise in machine learning or statistical modeling for biological data. Knowledge of enhancer-promoter interactions and 3D genome organization. All
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SUMOylation, transcription factors, or chromatin dynamics. Expertise in machine learning or statistical modeling for biological data. Knowledge of enhancer-promoter interactions and 3D genome organization. All
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for Knowledge-driven Machine Learning. We are looking for a motivated researcher, who has experience with both theoretical, methodological and applied research in change and anomaly detection in sequential data
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such as R, Python, Julia, etc. Familiarity with AI algorithms and Machine Learning Fluent oral and written communication skills in English Desired qualifications: Experience with research on epidemiological