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The students will be enrolled in the structured PhD programme in Computer Science at Sapienza University of Rome, Italy: https://www.uniroma1.it/en/offerta-formativa/dottorato/2025/computer-science About the
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and scalable. Design and build a technology demonstrator prototype of clinical-testing grade. Collaborate with interdisciplinary teams, including clinicians, engineers, and machine learning (ML) and
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disease through advanced imaging and biophysical approaches. The research group hosting this position studies Contractile Injection Systems (CIS) — natural protein machines used by bacteria to deliver
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. In addition, you must have: a solid foundation in energy technology and a strong understanding of artificial intelligence (AI), machine learning (ML), and data-driven modeling documented experience
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would choose to work with lecturers clustered into three broad areas: Science and Technology, Arts and Design, Civic and Community. Applicants from any field representing the Computer Sciences are welcome
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Leibniz-Institut für Präventionsforschung und Epidemiologie – BIPS GmbH | Bremen, Bremen | Germany | about 2 months ago
sciences Mathematics » Statistics Sociology » Other Psychological sciences » Other Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Germany Application Deadline 31 Mar 2026 - 23
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). The LSER Department is soliciting applications for a tenure track position at the assistant professor rank. Current assistant professors, as well as new PhDs, are encouraged to apply. Area of specialty is
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probabilistic frameworks. Experience with machine learning or AI methods for localization or perception (e.g. learning-based SLAM, data-driven sensor fusion) is a plus. Underwater or field robotics experience
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Department/Location: Department of Biochemistry, Central Cambridge PhD Position - Marie Curie network ON-Tract: Protein engineering of enzymes: in vitro directed evolution and machine learning-based
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new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health records