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As a Machine Learning Engineer, you will play a critical role in the development, deployment, and optimization of machine learning models with a strong emphasis on large language models (LLMs) and MLOps.
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Stay abreast of latest AI trends and maintain state-of-the-art knowledge in machine learning, deep learning, and computational linguistics. Experience with machine learning techniques and tools including deep learning, reinforcement learning, and unsupervised learning.
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Python Development: Have a minimum of 3-5 years of hands-on experience in Python development, with a particular focus on data science, machine learning, and AI. Moreover, at least 4-8 years of experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and proficiency in other Client algorithms and libraries.
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Knowledge of statistical sampling, optimization, machine learning, predictive modeling, and artificial intelligence techniques. Connects provisioned data across multiple sources and performs necessary transformations utilizing both technical, business, and process knowledge to transform data into meaningful values and insights.
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PHD candidates in Engineering/ IT/Computer Engineering/Computer Science/Bioinformatics or related discipline with relevant with a minimum of 1 years of experience in software development and pipeline automation in the pharmaceutical and/or biotech industry Required Skills Engineering skills to be able to optimize data analysis workflows for speed and usability Proficiency in programming languages such as Python and R, with experience in data analysis and machine learning libraries.
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The Asset Management Quant Research Engineering team is looking for a Director of Data Engineering who wishes to take on the challenge of building a world-class data operation.
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Familiarity with GCP, ICH and FDA requirements as applicable for Clinical Data Management. The successful candidate will provide clinical data management oversight of outsourced clinical trials including but not limited to project management, vendor management, coordination of internal reviews, and approval of deliverables.
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Key medicinal chemistry principles to your cross-disciplinary colleagues from Computational Chemistry, AI Research, Machine Learning Engineering, Cell Biology, and Pharmacology. At 1910 Genetics, we put computation at the heart of drugdiscovery, blending expertise in computational chemistry, structuralbiology, pharmacology, genetics, data science, and softwareengineering to develop drugs for previously undruggable targets.
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Expected Qualifications: Demonstrated knowledge and teaching experience in at least one of the following areas: Statistics, Data Analytics, Data Visualization with different tools, Python, Data Management & SQL, Data Engineering, R Programming, and/or Machine Learning.
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Analytical Skill, Analytical Skill, Analytical Thinking, Chemical Biology, Chemistry, Clinical Research, Clinical Trials, Collaboration, Communication Skills, Curiosity, Data, Data Analysis, Design Of Experiments, Detail-Oriented, Diversity & Inclusion, Drug Discovery, Ethics, Gene Therapy, Immunology, Laboratory, Lifesciences, Machine Learning (Ml), Mammalian Cell Culture, Medical Research, Molecular Biology {+ 11 more.
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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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Familiarity with emerging IAM skills like IAM in Multi-Cloud (AWS, Azure, Oracle, GCP) and Hybrid Environments, Identity Analytics and Machine Learning, Biometrics & Password less Authentication, Adaptive Authentication, Authorization, and Access Controls.
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Development and implementation of advanced data analytics and state-of-the-art machine learning models to generate insights from proteomics (ABPP) data, protein structure, and protein dynamics to enhance target prioritization and structure-based drug design efforts.
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The project aims to develop a multimodal AI framework to analyze the physiological signals and to decipher underlying molecular mechanisms by exploring other data sources, such as omics, large databanks, real world evidence, and clinical information.
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In addition to supporting client-facing projects, the practice is continually seeking to grow our in-house capabilities via innovative initiatives including robotic-process automation (RPA), data analytics, and development of diagnostics tools.
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machine learning data management robotic process automation jobs Title: director in Cambridge, MA
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