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Proficiency in programming languages such as Python, R, or SQL. Strong knowledge of machine learning techniques, statistical analysis, and data visualization tools Tableau, Power BI.
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Java, Python, ScalaExperience in Natural Language Understanding, Computer Vision, Machine Learning, Algorithmic Foundations of Optimization, Data Mining or Machine Intelligence (Artificial Intelligence.
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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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Methods vary widely but include applied mathematics, deep learning, machine learning, probability modeling, Bayesian reasoning, quantum computing, spatial statistics, spatio-temporal modeling, decision support, and data mining.
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They should have experience applying linear algebra, differential equations, dimensional reduction, machine/deep learning methods, and data mining and visualization to applied problems and geospatial data; be able to code in the Python or R language; and be capable of developing documentation and manuscripts for submission to peer-reviewed academic outlets.
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Basic to substantial experience in one or more of the following commercial/open-source data discovery/analysis platforms: RStudio, Spark, KNIME, RapidMiner, Alteryx, Dataiku, H2O, SAS Enterprise Miner (SAS EM) and/or SAS Visual Data Mining and Machine Learning, Microsoft AzureML, IBM Watson Studio or SPSS Modeler, Amazon SageMaker, Google Cloud ML, SAP Predictive Analytics.
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Demonstrated expertise in machine learning, deep learning, and NLP, with a strong portfolio in large language models like GPT-3, BERT, and RAG.Advanced proficiency in Python, with hands-on experience in ML libraries and frameworks such as TensorFlow and PyTorch.
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Apply computational tools and machine learning/deep learning techniques to analyze and interpret complex biological data relevant to drug discovery. Strong proficiency in programming (e.g., SQL, Python, R, MATLAB), database technologies (Oracle, mySQL, relational databases; graph databases are a plus), machine learning/deep learning (network architectures are a plus), dimensionality reduction techniques (e.g., PCA), and possible cheminformatics software suites Demonstrated experience in the analysis and visualization of large datasets.
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Conduct ongoing research to stay abreast of the latest developments in machine learning, deep learning, and data science, and apply this knowledge to enhance project outcomes.
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Utilize expertise in computational biology, machine learning, and bioinformatics to develop and optimize algorithms and models for the analysis of high-dimensional biological data.
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Causal Machine Learning, Causal Inference, Causal Discovery, Counterfactual, Causal Deep Learning, Causal Structure, Structure Learning, Structure Equation Model, SEM, Explainable AI, XAI, Data Science, Deep Learning, R, Python, Julia, Bioinformatics, Multiomics, Integrative Analysis, Medicine, Actigraphy, Pregnancy, Precision Medicine, Personalized Medicine, EHR, Electronic Health Records.
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Geospatial Analytics and Machine Leaning: You have demonstrated understanding of machine learning/deep learning frameworks, statistical concepts, and geo-statistics and spatial data analysis.
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Strong grasp of object-oriented programming (e.g. SOLID Principles)Experience using cloud computing for machine learning and deep-learning frameworks such as TensorFlow, PyTorch, KerasExperience in source management, code reviews, unit testing, and related modern software development methodologiesA distinguished publication record in NLP, Machine Learning, Computer Vision or related areas preferredAdvanced skills in data sourcing, cleaning, manipulating, analyzing, visualizing and modeling.
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Our specialized data centers run computing-intensive applications powered by clean energy for: Cryptocurrency Mining •Artificial Intelligence •Deep Learning •Machine Learning •Scientific Computing •Natural Language Processing •Video Transcoding and more.
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In this role, the Machine Learning Engineer will work with a variety of data-driven technologies, including traditional and deep learning paradigms. The Machine Learning Engineer should have a combination of generalist machine learning skills, engineering analysis skills, and the ability to quickly develop specialist capabilities where needed.
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machine learning data mining deep jobs Title: specialist
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