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Field
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with a PhD in computer science or bioinformatics are encouraged to apply. We create statistical, machine learning, and deep learning approaches for the processing of this data, with a major focus on
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of at least some of the following: – Extensive independent research experience – Creativity and independence – Experience analyzing hyperspectral data and developing machine learning models - Genetic
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research environment are essential. Preferred Qualifications: Prior experience with machine learning (ML) in high-energy physics is highly desirable, though not required. Appointment Details: The position is
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AI/machine learning, and data analysis using MATLAB or Python. Provide technical assistance on related research projects, such as preparing progress presentations and reports for funding agencies
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, cybersecurity, computer networking, machine learning, AI, ERP and cloud computing Conduct research with colleagues. Strong data analytics skills, including the ability to work with large sets of structured and
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antigens, T cell receptor (TCR) and antigen interactions and their crucial role in anti-cancer immune responses. You'll leverage your strong background in computational biology, machine learning, and
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approaches that address (for example) the intellectual status of the image, the political functions of art, or visual theology across multiple regions. The Mellon Fellow will teach two courses per year: one
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related to battery materials, correlated electron calculations, including via DFT+U, supercells, dynamical mean field theory or experience in defect and/or alloy calculations, machine learning, and other
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Large Language Models Natural Language Processing (NLP) Deep Reinforcement Learning Theoretical Foundations of Deep Learning and AI Optimization Techniques in Machine Learning Collaboration and Mentorship
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postdoctoral fellow interested in learning and using contemporary functional genomics approaches (CRISPR, RNAi, transgenics, etc.) to characterize the functions of genes involved in biological development