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data for European and international funding applications. Specific Requirements PhD in Physics, Engineering, Mathematics, Computing, Bioinformatics or related fields. Master's degree in Data Science
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» OtherEducation LevelPhD or equivalent Skills/Qualifications Who you are PhD in Computer Science, Machine Learning or related field or 5+ years of relevant industrial experience. Strong track record of developing
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Artificial Intelligence (AI) and Machine Learning (ML). In this position students will contribute to research projects in CKL and as part of their education, will also engage in a dedicated 6-months internship
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to state-of-the art AI/machine-learning, from modelling to measuring emissions and climate effects, noise and noise propagation. Job requirements The PhD position is funded by the Horizon-Europe TUNED
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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to communicate the accomplished / in-process work to internal and external stakeholders and adapt directions where needed. Where to apply Website https://www.imec-int.com/en/work-at-imec/job-opportunities/machine
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to apply Website https://cv.newton-6g.eu/ Requirements Research FieldEngineering » Computer engineeringEducation LevelMaster Degree or equivalent Research FieldEngineering » Electrical engineeringEducation
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to learn (or constantly hear) about machine learning methods for protein engineering and design Education and training You hold or are about to defend a PhD in Molecular Biology, Biotechnology, or a related
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Engineering » Electrical engineering Mathematics » Statistics Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 29 Mar 2026 - 23:59 (Europe/Oslo) Country Norway Type of
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limited to synthetic and chemical biology approaches to cellular computation and biomolecular logic design, the development and implementation of novel algorithms, machine learning for parsing biological