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Machine Learning Engineer, Nucleic Acids. This role will focus on MLOps for training in large scale ML models of RNA and/or DNA. You will be part of a dynamic, cross-functional team responsible for developing and deploying machine learning models for sequence design.
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Additional experience with Pyspark, AWS S3, Lambda, EC2/AZ, Data bricks, Machine learning (ML), GenAI/ Computer Vision, Theory of Probability, GitLab, R Programming, GCP & Airflow will be preferred.
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Design and implement deep-learning (DL) and machine-learning (ML) models to extract valuable insights from large repositories of time-series/biosensor data. This position is with the Signal Processing team at WHOOP. As a Signal Processing Engineer, focused on Deep Learning, you will be part of a cross-functional team composed of Signal Processing, WHOOP Labs, Firmware, and Data Science.
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As a Machine Learning Engineer at Nelnet, you'll be at the intersection of data science and software engineering. Feature Engineering: Collaborate with Data Scientists to engineer features for machine learning models.
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Familiarity with streaming data processing, real-time analytics, and machine learning pipelines. Proven experience building enterprise-grade software in a cloud-native environment (GCP or AWS) using cloud services such as GCS/S3, Dataflow/Glue, Data proc/EMR, Cloud Function/Lambda, Big Query/Athena, Big Table/Dynamo.
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As a Machine Learning Engineer, youll develop ML-based metrics for evaluating an autonomous vehicle system offline at large scale. Machine Learning development experience.
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Hands-on development experience with Design and Architecture of big data frameworks/tools: Azure Data Lake, Snowflake, Azure Data Bricks. Expert in Azure cloud computing, specializing in Azure data engineering stacks like ADF (Azure Data Factory), ADLS, Event Hubs, Snowflake, Databricks, streaming, Azure PowerShell, and Log Analytics.
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Collaborate effectively with renowned GSK experts in genetics, genomics, computational bio & chem, structural biology and medicinal chemistry, artificial intelligence / machine learning, and digital technologies to build the datasets, tools, models, and infrastructure necessary for oligo design & development.
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Massachusetts General Hospital (MGH) Department of Neurology and Harvard Medical School (HMS) is seeking a signal processing and machine learning post-doctoral fellow for a full-time position in the Gupta Lab. The fellow will be applying diverse methodologies to uncover information in multimodal data collected from individuals with common and rare neurodegenerative diseases.
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As a DataOps Engineer, you will play a significant role in the smooth operations of our global data platform by designing, implementing, and operating processes and tools that enable data innovation, increase trust in the data and speed up the development cycle.
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We seek a collaborative and highly innovative computational scientist to join our Predictive Sciences team and develop predictive models using cutting-edge machine learning/artificial intelligence techniques to discover novel therapeutic opportunities.
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Evaluates and recommends: upcoming industry trends, best practices, cutting-edge tools and novel programming approaches that leverage machine learning, simulation, robotic automation, metadata driven processes, artificial intelligence and natural language processing.
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Strong familiarity with topics of protein and macromolecular structures and interactions with other proteinsExperience with cloud environments such as AWSExperience in developing machine learning platforms using modern ML frameworks for deep learning (, tensorflow, keras, MXNet) & deploying in services such as Amazon Sagemaker.
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Experience in implementing machine learning algorithms in big data environments. Utilize Databricks for big data processing and streaming analytics. + Experience with big data technologies, such as Apache Hadoop, Spark, Kafka, and others.
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You will collaborate closely with partners in editorial, product, sales, and implementation to inform the development of engaging digital learning experiences, considering the potential of generative AI. You will champion evidence-based teaching methods, envision equitable digital solutions, and continuously iterate and improve our products based on insights from data and user feedback.
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big data learning jobs Title: machine learning engineer in Boston, MA
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Building a candidate pipeline through a great internship program for local college students and recent graduates at local universities is a great and cost-effective way to attract and retain top talent. By offering meaningful and impactful work experiences, regular feedback, coaching, and mentorship, you can create a positive internship experience that will make your organization a sought-after destination for future employees. This not only benefits the organization in the short-term but also in the long-term, as you'll have a pool of well-trained and experienced candidates who may be interested in full-time employment once they graduate. Furthermore, building relationships with local universities and college students can increase brand awareness and build a positive reputation for your organization in the local community.