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The ISR Division's projects focus on advanced signal processing, adaptive sensor technologies, advanced algorithms, machine learning, and modeling and simulation for applications that include airborne radar, electronic warfare, and undersea systems.
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Ph. D. in Bioengineering, Biochemistry, Biostatistics, Chemical Engineering, Computer Science, or similar discipline, with 0-5 years of experience and a strong focus on deep learning and/or cell engineering.
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In this role, you will collaborate with researchers to develop machine learning methods for computational biology and drive the implementation and testing of novel research ideas using cutting-edge deep learning techniques.
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Experience with analytic methods and technologies (artificial intelligence, machine learning/ deep learning/ neural networks, natural language processing, visualizations.
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Experience with natural language processing (NLP) is a plus. Publications or contributions to the machine learning community or open-source healthcare projects.
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We are seeking a highly motivated Machine Learning Engineer Co-Op from July 2024 to December 2024, focusing on Natural Language Processing (NLP) and Computer Vision applications.
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Experience with using (preferably open source) computational chemistry/chemoinformatics tools, e.g. RDKit, OpenMM, AutoDock Vina, as part of a small-molecule drug discovery campaign.
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We are looking for students who are self-motivated and interested in signal/image processing, machine learning, deep learning, statistical pattern recognition, and high-performance computing.
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Machine Learning : Proficient in Machine Learning, NLP, or deep learning using Python. Machine Learning : Experienced in Machine Learning, NLP, or deep learning using Python.
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The ideal candidate has a machine learning background, with previous experience building large-scale generative models, complex pre-processing pipelines, and comprehensive benchmarks to support scientific tasks.
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PhD and 2+ years of relevant research experience in developing machine/deep learning-based solutions and a sincere interest for computational life sciences. 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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DE conducting and streamlining quantitative research on signals and factors which predict future stock returns, using data science techniques including, large dataset manipulation, Webscraping, natural language processing, and econometrics and statistical techniquesmacro and micro economic reasoning, multivariate regression, and statistical machine learning.
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A strong background in at least one of the following areas: machine learning and AI, signal processing, biomedical and health sciences. The team leverages its experience in the fields of signal processing, computer vision, computational linguistics, machine learning, artificial intelligence, communication systems, and other areas of electrical engineering and computer science to create impactful solutions.
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The Bioinformatics Scientist is responsible for analyzing antibody selection data from yeast display, implementing machine learning algorithms to enhance data analysis and lead selection, and managing the data pipeline from raw file processing to database storage.
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Strong background in platforms and tools such as OpenAI, Azure Semantic Kernel, AWS Bedrock, and other machine learning technologies, with a focus on Generative AI and open-source AI frameworks.
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