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Keywords: Translational, Clinical, Imaging, Deep Learning, MRI, CT, Computer Vision, Data Science, Machine Learning, ML, DL, IHC, Immunohistochemistry. Apply classical and machine learning methods to derive insights from integrating and analyzing clinical response, tumor transcriptomic/genetic data, and radiographic image features.
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Conduct NLP (Natural language processing) using customized dialog flows intents, AI (Artificial Intelligent), machine learning, algorithms, trained predictive Modeling, and Deep Personalization on the UI (User Interface) using AEM (Abode Experience Manager.
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Querying, algorithms, data engineering, natural language processing, engine recommendation, experimentation, programming, data storytelling and intelligence, predictive modeling, and machine learning.
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Artificial intelligence, recruitment, machine learning, data science, staffing, ai, community, podcast, Computer Vision, Deep Learning, data engineering, big data, automation, ServiceNow, splunk, digital transformation, executive search, data analytics, cybersecurity, software engineering, software development, and it.
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How you’ll make an impact: As a Staff ML Ops Engineer at SiriusXM, you will be a key player in our Data Platform Team. Your role will be pivotal in deploying, managing, and optimizing machine learning (ML) models, leveraging advanced tools like Databricks and MLFlow.
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You have good knowledge and understanding of data science approaches including machine and deep learning; application in imaging domain (H&Es, IHC, radiomics) a plus. You have a strong knowledge and practical experiences with high density data types in a clinical setting (genomics, transcriptomics, proteomics, flow cytometry, TCR sequencing, immune assays, etc.
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Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch) Primary Location.
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Hands-on data scientist or machine learning engineer that will be expected to ideate, design, develop, model and deploy advanced solutions. Partners with broader Commercialization Data Science & AI Predictive Solutions organization to provide guidance on how advanced analytics and machine learning can be leveraged to solve ad-hoc non-commercialization needs.
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Your responsibilities will include designing and implementing machine learning models, collaborating with researchers and data scientists, and deploying models on AWS infrastructure. Machine Learning Engineer / Princeton, NJ / LLM / AI / NLP / AWS.
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Experience leveraging complex data to drive business decisions, hands on experience in data science methodologies (predictive analytics, machine learning, patient level data triggers) using R, Pytong, Databricks and deep knowledge of Qlik, PowerBI, Tableau for visualization.
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Collaborate with data scientist and machine learning engineers to deploy PyTorch and TensorFlow models on both CPU and GPU targets. Responsibilities include designing and implementing scalable distributed systems, tuning data systems for performance and reliability, and collaborating with other teams to deploy machine learning models.
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The Role: Sr Data Scientist- NLP, LLM and GenAI. Assist in Problem Solving: Troubleshoot complex issues related to machine learning model development and data pipelines and develop innovative solutions.
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Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment. AWS, AlienVault, Azure, Bash, Confluence, EKS, GSuit, Github, Golang, HTML, Hashcat, JIRA, JWT, Java, Javascript, Jenkins, Kubernetes, Metasploit, New Relic, Nmap, NodeJS, OWASP, Okta, PCI-DSS, PagerDuty, Python, Rails, Ruby, SAML, SQL, SQLMap, SignalScience, SignalSciences, Slack, Snyk, Sumologic, tenable.io, Twistlock, TypeScript, Wireshark, tcpdump.
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Experience in mathematics (statistics, linear algebra, differential calculus); data visualization (Tableau, Power BI, Qlikview); programming (SQL, Python, R, Java); data analysis (feature engineering, data wrangling, EDA) and machine learning (classification, regression, reinforcement learning, deep learning, clustering, dimensionality reduction.
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The Atmospheric and Oceanic Sciences Program at Princeton University, in association with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks a postdoctoral or more senior research scientist to conduct research on developing and using machine learned parameterizations developed from ocean-data assimilation increments.
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machine job Title: sr data scientist Company: Verizon in Lawrence, NJ
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