349 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" research jobs at Nature Careers
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key agroecosystem variables. These variables include cover crop growth, crop nitrogen, yield, and tillage practices. You will develop novel algorithms to integrate data-driven machine learning and
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, ATAC-seq, CUT&RUN, MERFISH, Visium), epigenomic data processing (chromatin accessibility, histone marks, enhancer mapping), multi-omics integration using Seurat, Signac, Harmony, ArchR or Scanpy, machine
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no.: 5342 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique balance of freedom and support. Join us if you’re passionate about groundbreaking international
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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Job description: The University of Vienna is a cosmopolitan hub for more than 10,000 employees, of whom around 7,500 work in research and teaching. They want to do research and teach at a place that
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(EHR), health information exchanges, and data analysis software. Experience with health IT innovation, including working with artificial intelligence, machine learning, telemedicine, or mobile health
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are proud signatories of the Armed Forces Covenant and welcome applications from service people. Further information For further information. please contact Professor Gordon Brown, email Gordon.brown
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immunology Experience with T cell engineering (CAR-T, TCR-T) and/or immunopeptidomics is preferred (but not required). At Dana-Farber Cancer Institute, we work every day to create an innovative, caring, and
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-Computer Interaction, Software Engineering, Computational Sciences, or a in a similar field Strong foundation in programming, algorithms, and experimental or applied research in a technical domain and
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the critical need for child-centric safe AI by developing a norm-first Belief-Desire-Intention (BDI) architecture where generative models (LLMs) are constrained by machine-readable child-protection policies