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: University degree in mechanical engineering, process engineering, chemistry, physics, or computer science Ability to quickly familiarize yourself with the various issues involved in energy storage and
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medical imaging data, computer models, and statistical tools. The individual will prepare manuscripts and contribute to the development of grant proposals. The McIntyreLab currently conducts research in
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FieldMathematicsEducation LevelMaster Degree or equivalent Skills/Qualifications Engineering degree or Master’s in statistics and/or computer science/applied mathematics LanguagesFRENCHLevelBasic LanguagesENGLISHLevelGood
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communication, organization, and interpersonal skills Attention to detail and accuracy Knowledge of digital image technology, photographic processes and ability to evaluate analog and digital image quality
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in advanced cardiovascular imaging procedures, including advanced transthoracic and transesophageal echocardiography (TEE), intraprocedural TEE, stress testing, Nuclear Cardiology, Cardiac computed
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program, you will perform grant funded research developing novel algorithms or building new dynamic imaging systems that account for patient motion (breathing, cardiac, etc.) during medical imaging and
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Field Engineering Researcher Profile First Stage Researcher (R1) Positions Master Positions Application Deadline 2 Feb 2026 - 00:00 (Europe/Madrid) Country Spain Type of Contract Permanent Job Status Part
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to contribute to a project at the intersection of biotechnology, drug development, and computational analysis. Candidate will be involved in: Collaborating with computer scientists and engineers to develop AI
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, performing experiments with students and postdocs, analysis of imaging and electrophysiology data, contribution to scientific publications. Please submit your application online at https://apply.ista.ac.at
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science/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning