Openness to learning basic neuroimaging concepts and DICOM data storage strategies - e.g., PACS. Most of the processes related to administration of center activities are handled by internally developed web applications (PHP and MySQL), with reliance on these systems to administer activities such as scheduling scan slots, setting up and maintaining user accounts, retrieving data, and distribute billing invoices.

Work you'll do The Data Scientist will join a team responsible for developing advanced analytics products; applying Machine Learning, Artificial Intelligence and statistical programming tools to enterprise data to advance and enable key mission outcomes.

The Information Technology and Applications Center (ITAC) of the National Heart, Lung, and Blood Institute (NHLBI), a component of the National Institutes of Health (NIH) at the Department of Health and Human Services (DHHS), is seeking exceptional candidates for the position of Data Science Architect (DSA.

This opportunity will involve a mix of skill sets ranging including a deep understanding of various machine learning, transfer and reinforcement learning techniques, developing theories supporting multi-agent learning, and transfer of learned behavior across heterogeneous agents.

The DSA offers an exciting opportunity to provide data and technology architecture leadership to the NHLBI Data Science community as NHLBI adopts more advanced computational techniques involving artificial intelligence and advanced analytics.

The Opportunity As a Computer Scientist/Engineer on the Fraud Team, you will be at the forefront of developing and executing an innovative strategy utilizing Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Large Language Models (LLM), and Multi-Agent Systems to combat a variety of fraudulent activities.

GAI's team members work side by side with government IT executives and OEM partners to solve complex business problems such as AI/ML, RPA, Cyber Security, Big Data, Data Center & Cloud.

Our team is seeking a Senior Data Scientist / Machine Learning Expert with specialized experience in Generative AI and Large Language Models (LLMs), chatbots, and natural language processing to contribute to transformative projects for a government client within the Department of Homeland Security (DHS.

Education RequiredHigh School or better PreferredBachelors or better s-p-m1 By applying, you consent to your information being transmitted by Monster to the Employer, as data controller, through the Employer’s data processor SonicJobs.

Deliver on projects in the areas of data management, data governance, dashboard monitoring, DQ dashboards, data controls, data lineage, and data mapping. and Transformation (automation, machine learning, artificial intelligence, reporting.

Examples will include techniques such as machine learning (ML) supervised and unsupervised learning, regression, neural networks and deep learning, natural language processing, etc.

The Director is a senior professional who works in conjunction with the Vice President for Lifelong Learning, with additional coordination with the Rabbi, the Executive Director and other staff and lay leadership to create a unified and dynamic educational program.

Support GDIT's FedCiv business areas by developing solutions to deliver capabilities, focusing on Data Science, Machine Learning, and emerging artificial intelligence (AI) capabilities. Deep Learning: Understanding neural networks, backpropagation, architectures like CNNs, RNNs, transformers, and tools like TensorFlow, PyTorch, or Keras.

This high performing team works with clients to implement the full spectrum of data analytics and data science, from data architecture and storage to data engineering and querying, to data visualization and dashboarding, to predictive analytics, machine learning, and artificial intelligence as well as robotic process automation (RPA.

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.