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Hands-on experience developing natural language processing (NLP) models, ideally with transformer architectures. Bachelor's / Master’s in Computer Science, Mathematics or Statistics, Computational linguistics, Engineering, or a related field.
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Experience in natural language processing / machine translation / text-to-speech. Experience in a science, technology, engineering or mathematics (STEM) related field, such as: Computational Social Science, Computer Science, Data Analytics, Operations Research, Statistics.
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Expert knowledge and practical experience in several of the following areas: machine learning, statistics, deep learning, natural language processing, informational retrieval.
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Experience with Natural Language Processing techniques. Exposure to Natural Language Generation. BSc/BA in Computer Science, Engineering or relevant field; graduate degree in Statistics or another quantitative field is preferred.
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Experience with Natural Language Processing. Bachelors degree in computer science, mathematics, physics, statistics, or related field. Advanced degree in in computer science, mathematics, physics, statistics, or related field.
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We employ scalable cutting-edge machine learning (Client), deep learning (DL), and Natural Language Processing (NLP) knowledge to better target customers and prospects, understand and personalize the content, and context needed to optimize their book-listening experience.
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Develops and applies all aspects of the use of natural-language processing (NLP) in software. The Annual Security and Fire Safety Report, containing policy statements, crime and fire statistics for all Indiana University campuses, is available online.
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Education & Experience Ph. D/MS degree in Machine Learning, Natural Language Processing, Computer Vision, Data Science, Statistics or related areas. Experience with natural language processing (NLP), and large language models, such as BERT, GPT, or Transformers.
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Experience implementing innovative machine learning and natural language processing techniques. Master's or PhD in a quantitative field (statistics, data science, computer science, psychometrics, educational measurement.
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Preferred Qualifications Proficient with at least one of the following packages: SAS, STATA, R, or PythonAbility to demonstrate expertise in multiple regression modeling and multilevel modelingStrong preference will be given to candidates with experience with network analysis, machine learning and natural language processing.
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Specialization in data-driven natural language processing, computational linguistics, and/or data science involving language, and an active research program. Interests can range from probabilistic approaches to language processing, to the investigation of societal consequences of big language data or the application of computational methods to social sciences, to mention but a few.
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Text mining and Natural Language Processing (NLP) is preferred. Knowledge of databases, data structures, data processing and data mining from large enterprise transaction systems Epic, Infor/Lawson, McKesson HPM, Payer Claims or similar applications in healthcare or other industries.
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Several faculty members in the EECS department are NSF-funded and publish in top research venues, with concentrations in big data, computer networks, natural language processing, machine learning, and artificial intelligence.
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3 or more years of practical working experience in one or more of the following areas: Natural Language Processing, Machine Learning Models, Question Answering, Text Mining, Information Retrieval, Distributional Semantics, Data Science, Knowledge Engineering.
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Experienced with traditional as well as modern machine learning/statistical techniques, including A/B testing, Causal Inference, Regression, Classification, Ensemble Methods, Deep Learning, Natural Language Processing, and Reinforcement Learning.
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As the job market remains highly competitive, we have seen a surge in "rage applying." This is when candidates apply to multiple jobs, often without considering whether they are truly interested in the role. Rage applying goes hand-in-hand with quiet quitting. Often, employees want to entertain the thoughts and feelings of leaving their job, but they aren't necessarily serious about leaving yet. Meanwhile, other employees engaging in this trend are actually trying to find a better role. As a recruiter, it can be hard to identify who are the real applicants in a sea full of quiet quitters, but understanding rage applying and identifying red flags will certainly help.
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In the current candidate-driven job market, recruiters are looking for unique ways to attract talent. Some have resorted to even (dare we say it?) recruitment strategies on the border of weird and wacky. What can we learn from the unusual recruitment tactics that are being used and actually getting results? Here’s a rundown of some unique recruitment strategies that actually work.
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