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Minimum of 3+ years of experience in data science with a focus on machine learning, deep learning, computer vision, NLP, and Spark. Utilize machine learning, deep learning, and other data science techniques to design, develop and optimize algorithms that drive data insights and product performance.
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We are encouraging applicants with strong research background in data science, machine learning, or natural language processing with a focus on biomedical applications. The Department of Health Informatics and Data Science and the Center for Health Outcomes and Informatics Research (CHOIR) at Loyola University Chicago invites applications for two immediate postdoctoral research positions opening in medical informatics for a one-year tenure.
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O Advanced knowledge of information security and systems, application controls and emerging technology utilization including but not limited to artificial intelligence, machine learning, deep learning, advanced process automation, cloud and application program interfaces.
$144,160 - $186,560 a yearFull-timeExpandApply NowActive JobUpdated 4 days ago - UpvoteDownvoteShare Job
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Projects include, but are not limited to, use of real-world data for modeling and prediction of disease severity and progression, discovery of disease subtypes, risk stratification, and precision medicine using traditional biostatistical methodologies and advanced methodologies from machine learning and/or deep learning.
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GDIT is seeking a Data Scientist to support its Technology Shared Services (TSS) Data Science team, developing predictive models, machine learning algorithms, and delivering data-driven solutions.
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One of the most commercially successful conversational AI companies in the world, Conversica is building the next generation of our artificial intelligence platform and we are looking for data science interns in the areas of natural language processing, natural language generation, and deep learning.
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Clinical Bioinformatics & Exploratory Analytics, as a part of the Clinical Data Science organization, supports drug development and other business use cases across Gilead by providing data analytical, engineering, and visualization solutions to enable scientific and other business exploration and decision-making, including statistical analysis, genomic data processing and analysis, general machine learning and deep learning, and other technical infrastructure.
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Strong technical expertise and thought leadership in Bioinformatic Pipelines, Computational Biophysics, AI/deep learning. Requires a PhD in related field or equivalent industry experience in related field such as Bioinformatics/Biomedical Engineering/Data Science/Computer Science/Statistics/Applied Math or related majors.
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Experience with RF engineering, machine learning, and signal processing, along with a deep understanding of communication systems is beneficial. The role will work closely with algorithm developers and customer stakeholders to utilize machine learning, signal processing, and data analysis methods to extract meaningful insights from RF (Radio Frequency) data and apply them to enhance intelligence and security operations.
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Applies data mining, NLP, and machine learning (such as supervised/unsupervised, Causal-ML, Online Learning, Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.
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The role focuses on expanding the Intelligence Data Science team, enhancing expertise in areas like natural language processing, anomaly detection, and deep learning. Strengthen the data science team's capabilities, particularly in natural language processing, anomaly detection, and deep learning.
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Collaborate with Architecture, Infrastructure and Data Science teams to translate their needs or challenges into production-grade Artificial Intelligence and Machine Learning deployment architectures for batch, real-time streaming deployments.
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Find even more open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general - ordered by popularity of job title or skills, toolset and products used - below.
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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.
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CUDA, Bash, and/or SQL), machine learning / deep learning methods, data analytics, and medical image analysis. The ideal candidate will have strong computational and analytical skills particularly in deep learning and is motivated by solving challenging medical research problems for patient benefit.
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