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the DFG Priority Programme “Molecular Machine Learning” and embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes”. The PhD
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of the following subjects: scalable data management, systems for machine learning, distributed and parallel systems, or cloud-based systems. We are especially interested in researchers who build working systems and
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, energy consumption, and packet loss. The use of distributed machine learning provides a relevant solution to mitigate the lack of communication reliability [3][4]. This PhD proposes to guide the learning
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or more of the following areas: AI and machine learning, natural language processing, large language models (LLM), experience in designing prompts, fine-tuning LLMs, or distributed systems. Good knowledge
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renewed each semester Requirements Minimum of enrolled/completed Master?s degree in a science field or current enrollment in a PhD program. Instructional experience of at least 2 years preferred. Past
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-edge tools and machine learning techniques to model spatial multi-omics data. The aim is to advance our understanding of protein dynamics at the single-cell level and contribute to a broader
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and deep understanding of machine learning, artificial intelligence, algorithms, and knowledge of the latest developments in AI. Proficiency in ML tracking/monitoring tools (MLflow, Grafana) and LLM
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this opportunity? Please email npp@orau.org Qualifications Preferred Qualifications for the Ideal Applicant PhD in Cryosphere Sciences or similar field Strong background in machine learning and neural
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Are you interested in understanding and modeling human capabilities to shape the future of autonomous systems? We are looking for a motivated PhD student to join an exciting research project focused
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Research and Imaging Centre (BRIC; https://www.plymouth.ac.uk/research/psychology/brain-research-and-imaging-centre ) . TARAs benefit from waived study fees and will work towards a PhD in an area of