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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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Postgraduate school experience in one or more of the following areas: bioinformatics, cheminformatics, computational chemistry and machine-learning. PhD degree in computer and information sciences, bioinformatics, cheminformatics, computational biology/chemistry with a minimum of 3 years' experience, or PhD in Biology, or life science field with minimum of 4 years’ experience in data sciences.
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Nobell is seeking a Computational Biologist / Machine Learning Scientist to join our team of scientists to contribute to the development of the Nobell Plant Grown Protein (PGP) platform.
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We have strong research and educational programs in Artificial Intelligence, Machine Learning and Data Science, Bioinformatics and Computational Biology, Human Computer Interaction, Robotics and Autonomous Systems, Software Engineering and Programming Languages, Systems and Networking, and Theoretical Foundations.
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The incumbent will apply advanced data analysis skills and machine learning in collaboration with a group of computational and biological scientists to perform translational oncology research around multimodal translational data sets for programs in clinical development.
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The Sr. Research Investigator in Computational Biology and Data Science will provide advanced analytical, bioinformatics, and machine learning support for Translational and Discovery projects in Translational Medicine.
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PhD, MS or BS in computer science, electrical engineering, biomedical engineering, bioinformatics, computational biology, physics, mathematics or equivalent. and machine learning and deep learning frameworks (e.g. TensorFlow, Keras, PyTorch.
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Stay updated on protein space and industry trends: Keep abreast of the latest developments in computational structural biology, protein engineering, and related fields. 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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Amgen leverages commercially available and proprietary machine learning (ML) platforms to connect our research scientists with new insights about target or disease biology, therapeutic candidates, and ultimately to help shape our therapeutic portfolio.
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The Computational Toxicology Intern will gain exposure to a wide range of safety-related datasets ranging from biology, pharmacology, toxicology, and chemistry to help with predictive modeling, data engineering, and data visualization.
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PSC advances science across a wide spectrum of fields, including artificial intelligence/machine learning, medical imaging, weather modeling, cell biology, and genomics. Masters and/or PhD in Biology, Bioinformatics, Computational Biology, Computer Science, Biomedical Engineering or related fields.
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You have a Masters or PhD in Data Science, Machine Learning, Applied Statistics, Bioinformatics, Computational Biology, Computer Science, Physics or closely related disciplines.
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Develop and publish relevant bioinformatics methodologies, pipelines, and algorithms, and stay abreast of the latest advancements in bioinformatics, machine learning, and oncology research.
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Develop new computational, machine learning, and statistical analysis methods for integrating and analyzing large-scale, scRNseq, CRISPR and genotypic data using current state of the art technologies.
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Who You Are: You are a dynamic Computational Scientist who will work with other scientists to apply cutting-edge computation, Machine Learning/Deep Learning approaches to revolutionize our large molecule computational tools by contributing to accelerating and improving the process of design and engineering of novel biologics drug candidates.
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