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managing large amounts of data by designing structured databases (PostgreSQL, MySQL). Machine learning methods such deep learning for analysis of proteomics data and classification of cancer profiles. Since
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results relevant for immuno-oncology and cancer immunotherapy. Eventually these analyses pipelines will drive creation of new immune landscape scoring metrices via state-of-the-art machine learning
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sciences. Areas of interest include but are not limited to: AI-driven drug discovery and development, quantitative systems pharmacology, large language models for education and clinical support, machine
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(AI/Machine Learning (ML) expert for an academic investigational position. The Department of Data Science at the Dana-Farber Cancer Institute (DFCI) and the Department of Medical Oncology seek
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machine learning models for the diagnosis of temporomandibular disorders (TMD) based on jaw motion time series data. Moreover, the successful candidate will be affiliated with the Comprehensive Center AI in
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dedicated to discovering and refining the core mechanisms that will enable machines to learn continuously, make robust decisions in complex environments, and evolve autonomously. Key research directions
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Context and Motivation Bilevel optimization problems, in which one optimization problem is nested within another, arise in a wide range of machine learning settings. Typical examples include
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simulation techniques will be used to design proteins; a particular focus is binding flexible regions and antibody design, which are challenging for current machine learning approaches. You will become
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of computational tools including Artificial Intelligence (AI) and Machine Learning (ML) in virtual screening, small molecule design and optimization is preferred. Set up the chemistry strategy for projects and
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Associate Scientist or Lead Researcher - (Protein Engineering and Design, Genome Editing, Biotechnol
protein candidates by structure-guided design and high-throughput screening. Apply new artificial intelligence and machine learning techniques to guide protein engineering design. Analyze experimental