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organ-on-a-chip (OOC) models, colony picking and bioprinting). The ideal candidate should have strong expertise performing machine learning (ML), computational biology with the capability and/or
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Are you interested in developing machine learning for scientific research? We invite you to apply to undertake postdoctoral research in the group of Professor Craig Butts, developing machine
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improving plant health using machine learning and artificial intelligence. Mentor(s): The mentor for this opportunity is Yulin Jia (yulin.jia@usda.gov ). If you have questions about the nature of the research
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skills and a keen interest in application-oriented academic research in applied machine learning. The selected candidate will conduct a research project on data-driven fraud detection, with the objective
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learning) to advance in-silico drug discovery. If you are a senior AI/MD expert with experience in applying machine learning to scientific simulation and a passion for driving innovation, we want to hear
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Max Planck Computing and Data Facility (MPCDF), Garching | Garching an der Alz, Bayern | Germany | about 1 month ago
for a Data Scientist or Machine Learning / AI expert (m/f/d) Kennziffer 07/2025 to join the AI and High-Performance Data Analytics division of MPCDF. In this role, you will contribute to a variety of
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further both professionally and personally in an interdisciplinary setting. Position DWI is looking to fill the position as soon as possible: Research Scientist Machine Learning Engineer - AI-Powered Image
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title: CSC2515HS - Introduction to Machine Learning Please note, this position is a 0.5 FCE appointment. Course description: Machine learning (ML) is a set of techniques that allow computers to learn
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DWI Leibniz-Institut für Interaktive Materialien e.V. | Aachen, Nordrhein Westfalen | Germany | about 2 months ago
looking to fill the position as soon as possible: Research Scientist Machine Learning Engineer - AI-Powered Image Analysis The Project HairSense is a translational project at the DWI – Leibniz Institute
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organization-specific. This poses a critical challenge for applying machine learning: there are typically only a few examples of each specific fraud pattern to learn from. Classic machine learning methods