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Employ advanced AI and machine learning models, including deep learning and predictive analytics, to identify novel materials and predict their performance within battery systems.
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A Glimpse into the Daily Routine of a Machine Learning Engineer. Self-motivated and proactive, with a passion for learning and staying updated with the latest trends and advancements in the field of data engineering.
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Individuals in this role are expected to be recognized experts in areas such as artificial intelligence, machine learning, computational statistics, and applied mathematics, particularly including areas such as deep learning, graphical models, reinforcement learning, computer perception, natural language processing and data representation.
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Machine Learning and Deep Learning: Good understanding of: ML algorithms like linear regression, logistic regression, etc., supervised, unsupervised, and reinforcement learning, AI Frameworks like TensorFlow, PyTorch, scikit-learn etc., Neural network, NLP, computer vision, and predictive analytics.
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This is a unique opportunity for a machine learning scientist to directly impact patients’ lives in a clinical setting. 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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AI and Machine Learning: Utilize state-of-the-art AI and machine learning techniques, including natural language processing (NLP), to train the chatbot for accurate and effective communication.
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Big data/machine learning and AI techniques, including neural network and deep learning frameworks and machine learning tools (e.g., TensorFlow, Theano, Torch, Keras.
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Have functional knowledge related to some of the following: modeling knowledge of financial risks and derivative products (e.g., equity, FX, commodities, credit and interest rates), risk management, model development, model validation, advanced analytics (e.g., machine learning techniques.
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As a Lead, Machine Learning Engineer, you will partner with Data Scientists, Data Engineers, Data Analysts and other professionals to implement machine learning models that will deliver stability, producibility, scalability and integration with other products and services.
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Experience leveraging complex data to drive business decisions, hands on experience in data science methodologies (predictive analytics, machine learning, patient level data triggers) using R, Pytong, Databricks and deep knowledge of Qlik, PowerBI, Tableau for visualization.
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We are seeking a seasoned Senior Data Scientist specializing in cloud compute computer vision to lead our computer vision based analysis efforts, The ideal candidate will spearhead the development and implementation of advanced computer vision algorithms and machine learning models.
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Current experience and interest in machine learning/artificial intelligence (surrogate or metamodeling) from a coastal, hydrologic, geoscience or natural hazard perspective; This includes probability analysis, machine learning, storm climatology, database management, civil engineering, and coastal engineering.
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Strong proficiency and experience in one or more of the following areas are essential: single-cell genomics, machine learning, multi-variable statistics, open-source software development, or bioinformatics.
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Our client is a pioneer within the programmable RNA medicine space and are looking for an expert of Machine Learning, Artificial Intelligence, and RNA Biology to help shift the paradigm of novel drug development.
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Develop, implement, and optimize machine learning models to detect money laundering, fraud, and other financial crimes, leveraging techniques such as anomaly detection, clustering, and predictive modeling.
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machine learning jobs Title: scientist Company: National Geospatial Intelligence Agency in New York, NY
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