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Field
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to undertake world-leading research in the design, integration and Edge-implementation/testing of multimodal machine learning models. Your experience in real-time implementation of federated AI and Edge-based
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thermodynamics Design and implement machine learning models for data collection, reduction, analysis, and visualization. Work creatively, independently, and productively. Work as a member of a multidisciplinary
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. You will also be responsible for implementing the model as a computer simulation and analysing it within a health-economics framework using standard computational techniques. You will also be
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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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, molecular dynamics, and machine learning, to model battery electrolyte and solid electrolyte interphase (SEI), while collaborating with experimentalists. Qualifications • Ph.D. in Computational Materials
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organoids will be plus. Dry lab: Highly motivated candidates with a PhD/MD degree in bioinformatics, genome science, systems biology, biomedical informatics, computational biology, machine learning, data
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to develop and optimize scalable experimental protocols across diverse material families. This role is part of a multidisciplinary team integrating materials chemistry, machine learning, and autonomous
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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 expected to be based
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testing of machine learning/AI algorithms Integration of radiomic and biological datasets Working closely with Medical Physics colleagues on reviewing recommendations for detection of specific metabolites
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dynamics. Expertise in machine learning or statistical modeling for biological data. Knowledge of enhancer-promoter interactions and 3D genome organization. All candidates and projects will have to undergo a