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of induced pluripotent stem cell-based human disease models 2) Understanding the role of immunity during tumorigenesis as well as disease relapses in order to design novel inhibitors, CAR-T cell therapy and
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Essential skills, knowledge and experience: Experience with machine/deep learning development Data-Centric AI Knowledge Notions of cybersecurity and networks are optional Spoken and written English Desirable
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, genomics, computer science, bioinformatics, or a related discipline. The successful candidate will lead computational research projects applying advanced statistical, machine learning, and artificial
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emerging solid-tumor indications. This position is ideally suited for a recent PhD or MD/PhD graduate seeking hands-on training in CAR T cell engineering, patient-sample analysis, and translational
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duties incidental to the work described herein. Job Requirements PhD in a related scientific area (e.g., AI, Deep Learning, Machine Learning, Statistics, Database, Bioinformatics, Computational Biology
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Lab at Princeton University aims to recruit a postdoctoral fellow or more senior research position to work on projects related to the development of AI/machine learning approaches for chemical and
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innovation. Colourbox via Unsplash Colourbox Qualification requirements Required qualifications: PhD in machine learning, statistics, computational biology, applied mathematics, physics, or similar. Eligible
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: PhD degree in Computer Science, Electrical Engineering, or a closely related field Strong research background in computer vision and deep learning Solid experience with multimodal learning, segmentation
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reports and grant proposals. You should possess a PhD or DPhil (or near completion of) in Machine Learning or Maths. Informal enquiries may be addressed to jakob@robots.ox.ac.uk For more information about
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“Quantifying Efficacy and risks of solar radiation management (SRM) approaches using natural analogues”. The project will use novel machine learning-based methods to determine the climate response to a range of