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Field
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active role in developing novel machine learning based systems and tools on the path towards clinical use and implementation of AI for the treatment and care of individuals also from minority populations
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Responsibilities: Conduct research on the design and analysis of scalable machine learning systems using convex/nonconvex optimization and federated learning methods. Develop algorithms and prototypes
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++, or Go, and frameworks like PyTorch or TensorFlow, is highly advantageous. Experience in developing and deploying machine learning models, particularly in natural language processing (NLP) and large
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approaches, machine learning) where appropriate. The successful candidate will actively promote FAIR data practices and will have opportunities to contribute to teaching, training, and wider community
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work that underpins the scientific research of the collaboration. Research Title: Process Modeling using Physically Informed Machine Learning The work will entail: § Designing and training physics
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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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well as in a collaborative, team-oriented environment. Preferred Qualifications: Prior experience working with mouse models of cancer is strongly preferred; candidates without prior experience will be
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machine learning model development; Experience designing research projects and managing project delivery; Experience processing, documenting, and analyzing data sets; Ability to excel in a highly
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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, glioblastomas, colon cancer, and lung cancer. Advancing precision oncology through machine-learning models: We integrate multimodal patient data, including multiomic data and health record information, to develop