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experimental approaches, including machine learning, genomic assays, and live imaging of subcellular dynamics coupled to CRIPSR-based genome engineering. Much of the experimental work is carried out in live
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, and written communication skills evidenced by a publication record in the area of control theory, mathematical optimization, AI, or machine learning. Preferred Qualifications: Publication record in
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can be found on his website https://knightlab.ucsf.edu/ About the role: We are looking to hire a full-time lab manager who has experience working with rodents. A core responsibility of the position (~50
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-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
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of the Research Project(s): The goal of our research is to develop vaccines and therapeutics targeting human pathogens. Duties and Responsibilities: 20% Structure-based and machine-learning immunogen design for
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with experience in ligand discovery. Our research group is focused on developing state-of-the-art computational methods for ligand/drug discovery, using machine learning, high-performance/cloud computing
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members have been working on statistics learning, granular computing and knowledge discovery, machine learning, deep learning, and specifically interpretable artificial intelligence. Many innovative
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environment where machine learning meets real-world scientific impact. What You’ll Do: Conduct cutting-edge research at the intersection of AI and science Develop large-scale deep learning models for scientific
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minimizing computational and energy costs. The proposed approaches will rely on machine learning methods applied to image analysis, with the objective of enabling early identification of at risk areas and
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of machine learning What We Offer: On the basis of full-time employment (40 hours/week) the minimum salary in accordance with the collective agreement is € 5,014.30 gross per month (14 x per year, CA Job Grade