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Field
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relationships and apply data-driven or machine learning approaches to guide molecular design and accelerate materials discovery Collaborate with internal and external stakeholders, including computational, data
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of metabolism, membrane transport, cellular regulation, structural systems biology, machine learning and disease mechanisms. These positions are embedded in the transition from CeMM in Vienna to the newly founded
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the NYUAD Kawader program, for details regarding the program, open dates, specific program requirements, and FAQ's please refer to our NYUAD Kawader webpage: https://nyuad.nyu.edu/en/about/careers
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on integrating an agentic platform for electrocatalyst discovery and developing novel machine-learning algorithms (e.g., reinforcement learning) for materials discovery and process optimization. The candidate will
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for tumor behavior and clinical outcomes Development and implementation of artificial intelligence and machine learning algorithms for biologically and clinically motivated questions in pediatric oncology
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, machine learning, programming, software engineering, instrumentation, benchmarking, reproducibility and technological prototyping. The mandatory requirement for technical-scientific proficiency in English
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on the design and development of mechanically intelligent surgical tools that simplify surgical motions and incorporate image sensing. These tools will be integrated with robotic platforms and machine learning
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Ph.D. in neurobiology or a related field. 1 year post-doctoral experience Certificates/Credentials/Licenses Applicants must have a Ph.D. in neurobiology or a related field Computer Skills
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, selectivity and phenotype mechanisms directly in primary human cells and employ machine learning methods for data analysis. This includes activity-based profiling, global- and phosphoproteomics and phenotype
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for relevant systems of EF-hand proteins · Supervise and instruct graduate and undergraduate students in Dr. Pengfei Li’s lab Minimum Education and/or Work Experience A MD or PhD is required in a closely related