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Proven hands-on experience with Data and Statistical Analysis tools and techniques (SAS, R), Predictive Analytics, Artificial Intelligence (AI), Natural Language Processing (NLP) and deep learning frameworks to enable more precise decision making in clinical monitoring to enhance study quality, add efficiency, and improve patient safety.
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Promote modern approaches in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Large Language Models (LLMs) within the computational and data ecosystem to advance researcher productivity and scientific discovery.
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Proven experience in Deep Learning, Natural Language Processing, Large Language Models, Search & Recommendation. Develop Natural Language Processing and Gen AI models to improve effectiveness of customer support.
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Proficiency in deep learning frameworks such as TensorFlow, PyTorch, or similar, and strong hands-on experience with large language models (e.g., GPT, LLaMA, MPT). SAIC takes on grand scientific and engineering challenges in machine intelligence and actively contributes to the international research community through scientific publications and presentations in major conferences and journals in research areas of Computer Vision, HCI, Contextual/multi-modal modeling, NLP, Speech Recognition, Dialogue, and Machine and Deep Learning.
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MS in Computer Science, Information systems, or Computer engineering, Systems Engineering with relevant experience in Text Mining / Natural Language Processing (NLP) tools, Data sciences, Big Data and algorithms.
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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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Our projects include machine learning-powered products, cloud computing infrastructure and strategy, open source stewardship, natural language processing, and more. Quant analytics libraries for users of BQuant and the rest of our team who are creating specific workflow solutions on top such as natural language processing (NLP), backtesting, trading systems integration etc.
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We are looking for a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas.
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Data Scientists contribute to projects involving one or more of the following disciplines: Data Science, Applied or Theoretical Machine Learning, Deep Learning, Deep Reinforcement Learning, Physics Inspired ML, Classification Systems, Natural Language Processing, Artificial Intelligence, or Computer Vision.
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Overview Are you a a data expert with broad understanding and experience in solving business problems by integrating statistical analysis, machine learning/artificial intelligence, deep learning, Natural Language Processing (NLP) and Large Language Models (LLMs.
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The solutions developed draw on IT-RI’s strong capabilities in statistical modeling, machine learning, natural language processing, information retrieval, statistical genomics, data mining, and big data management and analytics.
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Understanding and hands on experience in Large Language Models, Natural Language Processing, Computer vision, Image Processing, and other generative AI techniques.
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Our work depends on TS/SCI cleared Machine Learning Engineer joining our team to support our intelligence customer in Springfield, VA or St. Louis, MO. Deliver simple solutions to complex problems as a Machine Learning Engineer at GDIT. Here, you'll tailor cutting-edge solutions to the unique requirements of our clients.
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UNPs approach couples machine learning-based in silic o tools with massively parallel peptide chemistry to enable the discovery and rapid optimization of highly non-natural macrocyclic peptides aimed at extracellular targets, undruggable intracellular oncology targets, and beyond.
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These may include, but are not limited to, predictive analytics, prescriptive analytics, natural language Processing (NLP), machine learning, generative AI, etc. Familiarity with data technologies such as data visualizations, Natural Language Processing (NLP), Machine Learning, generative AI. Comfort with big data approaches, such as data lakes, desirable.
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machine learning natural language processing hands on jobs Company: Bytedance
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