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, micro-CT, particle size analysis, calorimetry, and synchrotron experimental measurement techniques. Knowledge of AI-based and machine-learning methods is also beneficial. For further information about a
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Fundacio de Recerca Clinic Barcelona-Institut d'Investigacions Biomediques August Pi i Sunyer | Spain | 13 days ago
: miRNA-regulated logic-gated multispecific CAR-T cells to overcome tumor heterogeneity and stromal barriers in solid tumors Desirable criteria: Master’s degree in Biomedicine, Immunology, or a related
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Engineering, Bioinformatics, Statistics, Applied Mathematics, Physics, or a closely related STEM field. Demonstrated experience developing AI and machine learning models for biomedical applications. Job
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system integrates robotics, automated sample handling, sensor networks, imaging systems, cloud computing, and machine-learning-based analytics. The research work at NTNU will focus particularly
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archaeological and historical contexts is also required. Additionally, the ability to perform *ad hoc* data processing (multivariate statistics, machine learning, etc.) is desirable. Proficiency in programming
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to apply Website https://www.academictransfer.com/en/jobs/363306/phd-ai-based-cardiovascular-ima… Requirements Specific Requirements You are a motivated researcher with a strong technical background and a
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information exchange between operators, AI systems, and DPP infrastructures.A central research focus will be the exploration of dynamic and machine‑interpretable data formats that allow DWIs to be generated
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Position Description The LHC Physics Center (LPC) at Fermilab (https://lpc.fnal.gov/ ) seeks applications for CMS LPC Distinguished Researchers (DR) in both the Senior and Junior category
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: • Scientific achievements in engineering and technical sciences • Additional qualifications (in line with the education held) confirmed by a certificate Where to apply Website https://up-sanok.edu.pl/inne/oferty
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Health Data Sciences and Informatics (OHDSI, https://ohdsi-europe.org ) initiative, dedicated to bring out the full value of observational health data through the OMOP Common Data Model and large-scale