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Should be competent with at least one numerically focused programming environment such as Python or Matlab using the native linear algebra, image processing, and machine learning libraries.
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Analytical Skill, Analytical Skill, Analytical Thinking, Chemical Biology, Chemistry, Clinical Trials, Collaboration, Communication Skills, Curiosity, Data, Data Analysis, Design Of Experiments, Detail-Oriented, Drug Development, Drug Discovery, Ethics, Gene Therapy, Immunology, Inventory Management, Laboratory, Lifesciences, Machine Learning (Ml), Management, Medical Research, Molecular Biology {+ 10 more.
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This candidate should be proficient in using BI tools such as Tableau, in addition to experience with one or more modern technologies such as relational databases, AWS, Snowflake, Python, Databricks, Git, and Machine Learning.
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Work with research informatics and data teams to deploy enterprise-level cheminformatics and machine learning solutions across the discovery and medicinal chemistry groups. This will include implementing machine learning methods for physicochemical property predictions as well as ADME/PK/tox endpoints.
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We are looking for a proactive self-starter with extensive expertise in data operations at scale, a deep understanding of AI, chatbots, and large language models (LLMs), and a strong commitment to continuously enhancing our machine learning data pipelines and infrastructure.
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The ideal candidate should have an outstanding scientific reputation in the field of machine learning research for computational biology and a demonstrated passion for solving biological problems relevant to drug discovery.
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Reporting to the Senior Director / Head of IT, you will work alongside Data Science, Research, Clinical, and TechOps teams to spearhead the architecture design and deployment of data products, supporting the goals of Data Science, Bioinformatics, and Machine Learning.
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Experience working collaboratively with computational biology and machine learning scientists for de novo protein design. MIMIC™, our computationally driven bioplatform that couples machine learning with molecular mimicry, engineers therapeutics to trigger a desired biological response.
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Our core technical competencies include system-level modeling and gap analysis, advanced sensing and signal processing, machine learning and artificial intelligence, computational modeling, hardware and software prototyping, model-based systems engineering, and human data collection in laboratory and field environments.
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Experience with analytic methods and technologies (artificial intelligence, machine learning/ deep learning/ neural networks, natural language processing, visualizations) Demonstrated proficiency with data science languages (R, Python, SQL, Julia, SAS) and visualization tools (Tableau, Spotfire, SAS, JMP, RShiny.
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Expertise in one or more of the following or related areas: optimization methods, hardware and electronics, machine learning tools, computational physics, and signals and systems. Expertise in one or more of the following or related areas: optimization methods, hardware and electronics, machine learning tools, computational physics, and signals and systems.
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Demonstrated experience with machine learning algorithms (regression, semi-supervised learning, deep learning, reinforcement learning, time series analysis, predictive modeling, data mining, cognitive computing.
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The Human-centered AI team in Digital Health Oncology R&D is looking for an Associate Director of Data Science to specialize in the development of novel digital endpoints using data science, statistical learning, and innovative machine learning methods on multi-modal datasets including clinical data, real world evidence, imaging, wearables, and other biomedical data sources to address patient burden.
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With its proprietary FORCE™ platform, Dyne is developing modern oligonucleotide therapeutics that are designed to overcome limitations in delivery to muscle tissue. The Clinical Data Analyst partners cross functionally to identify risk to data integrity throughout the data flow to drive quality collection for non-EDC data sources (fluid & tissue labs, digital, imaging, wearables) in Dyne's clinical studies.
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Master's Degree in Engineering, Mathematics, or Computer Science- with a focus in Process Mining, Machine Learning, Data Science, and Artificial Intelligence. Build business insights using data visualization & machine learning in applications that are used by Fortune 500 companies.
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