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General familiarity with one or more of: natural language processing, transformer architectures, machine vision (object detection, facial recognition), one-shot learning, reinforcement. Solid background in statistics and/or mathematics (particularly linear algebra and calculus), preferably including sampling and bias measurement and mitigation.
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We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day.
$231,000 - $339,000 a yearFull-timeExpandApply NowActive JobUpdated 5 days ago - UpvoteDownvoteShare Job
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A wide range of technical expertise finds a natural fit at NSA including, but not limited to, fields of algebra, combinatorics, Fourier analysis, operations research, graph analytics, regression, machine learning, exploratory data analysis, stochastic processes, statistical modeling, forecasting, and cryptography.
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Coursework or inclination in research and teaching differential equations, numerical analysis, abstract algebra, probability, statistics, and data science. Professor of Mathematics is a full-time, tenure track position in the Mathematics Program in the Department of Natural and Computational Sciences in the College of Arts and Sciences.
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Contribute to the design and implementation of new features, leveraging deep learning, natural language processing, computer vision, and reinforcement learning techniques. Strong mathematical background, including knowledge of linear algebra, calculus, probability, and statistics.
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Applied Research Mathematician/ Mathematical Statistician - Entry to Mid Level (Maryland) NSA is unique in its ability to offer the opportunity to work on cutting edge problems in a wide range of technical fields including: - Data Science - Machine Learning - Human Language technology - Cryptology - Signals analysis - Speech/image/video processing - Network analysis - Cyber security - High performance computing All employees at NSA have access to diverse opportunities with the expectation that they will continuously grow as professionals.
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Basic knowledge in Linear algebra, Optimization, Probability Theory, Calculus. Potential applications will include, but not be limited to, computer vision, natural language processing, climate, etc.
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A solid background in at least 3of the following areas: Natural language processing, statistical ML techniques, graph algorithms, constraint optimization, signal processing (speech or vision), deep learning, and distributed systems.
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Experience with deep learning, natural language processing, computer vision, or reinforcement learning. Strong mathematical background (linear algebra, calculus, probability, and statistics.
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The campus is located in Northern California, 90 minutes from the state capital, Sacramento, and a three-hour drive from the San Francisco Bay Area. Chico offers year-round natural beauty, outdoor leisure activities, and a thriving arts, music, and events scene.
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The National Security Agency (NSA) is part of the DoD Intelligence Community Defense Civilian Intelligence Personnel System (DCIPS). Degree must be in Mathematics, Physics, Engineering, Data Science, Computer Science, Statistics, or a related STEM field.
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The models will span domains from machine learning (computer vision, natural language processing, recommendation engines, reinforcement learning) to high performance computing (linear algebra.
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Understanding of statistics / linear algebra / quantitative research methods - Ad-Hoc. Certifications/licenses: Machine Learning and Natural Language Processing. Skilled in SQL, Adobe Acrobat, PowerPoint, Python, Excel, WebEx, Word, R, Linux, GIT/GITHub, deep learning framework (Pytorch), data science packages (Numpy, Pandas), AWS, transformers (Hugging Face), chatbots and dialogue systems (recommended.
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A solid background in at least 2 of the following areas: Natural language processing, statistical ML techniques, graph algorithms, constraint optimization, signal processing (speech or vision), deep learning, and distributed systems.
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Computer programming (e.g., scripting, data parsing/ETL, artificial intelligence, machine learning, math/statistics packages, natural language processing, software versioning, distributed computing.
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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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Hiring Transparency
Transparency in hiring refers to the open and honest communication and information sharing that takes place between employers and job candidates. It encompasses all aspects of the hiring process, from posting job descriptions to providing feedback on performance during and after the interview process. In today's job market, hiring transparency has become increasingly important for both employers and candidates alike.
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If you’re like most of us, you’d love to be wealthier. Having more money would alleviate stress. It would make it easier to pay your bills and buy nicer things. Maybe it’d allow you to spend more time with your kids and go on more vacations. You’re not alone if you wish you could somehow earn a more significant income.