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leverage state of the art machine learning models (AlphaFold2, RFdiffusion) and multi-omics data integration to guide the rational design and optimization of therapeutic antibodies. Overall, you will have
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, organised researcher who can evidence: A PhD, or equivalent in statistics, machine learning or a closely related discipline, OR near to completion of a PhD. Expert knowledge of statistical inference methods
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. Experience with Python programming. Familiarity with machine learning methods. Strong communication skills and ability to work collaboratively across theory and experiment. Desired Qualifications PhD in
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statistical modeling, machine learning, data analysis, and reporting Proficiency in Python or R Ability to plan, execute and control a project, establishing realistic estimates and reporting timelines Advanced
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of interest include, but are not limited to, stochastic, discrete, large-scale, and data-driven optimization, machine learning methods for sequential decision making, or stochastic modeling and prescriptive
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-assisted image analyses. Candidate must be capable of working independently but importantly must be willing to work as part of a team. The RAI will participate actively in bench research as well as data
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or equivalent Skills/Qualifications The work and responsibilities of the researcher will include the following research topics: Knowledge in: Computational neuroscience Machine learning / AI Biomedical data
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Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
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assessments. Develops models and examples of innovative activities and assignments that make use of AI tools. Provides consultation support for faculty and instructors on teaching and learning issues related
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knowledge in areas related to Data Science; Have knowledge of Machine; Be a PhD Student in Information Management. Work plan and goals to achieve The work plan will include the development of Machine Learning