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complex business questions into actionable insights—including data wrangling, visualization, modeling, and interpretation of results. Demonstrated experience building statistical and machine learning models
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Research Associate (Computer Engineering/Computer Science/Engineering/Mathematics/Biology/Chemistry)
. Key Responsibilities: Develop deep learning models and algorithms for biomedical and healthcare applications. Data processing for training of deep learning models. Mentor junior research lab members in
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intestinal duct section. To achieve this, we will address the inverse design problem using physics-informed machine learning that consists of determining the optimal structure and material distribution
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application of natural language processing (NLP) and other machine learning methods, including training and deploying large language models (LLMs) for extracting data from electronic health records (EHRs
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interface models and patient-derived organoids to study the contribution of G protein coupled receptors to oral cancer pain. Research projects will focus on applying the models to investigate the cancer
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programming (in integers) or constraint programming, numerical simulation tools and machine learning techniques. Secondly, using mathematical modelling approaches, an internal porous network will be developed
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the project scope and agreements. Scientific activities include: 1) Develop Tidal Resource Models Design and implement statistical and machine learning tidal models to predict energy demand, supply, and market
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models. By utilizing biobanks of lung biopsies and established animal models, LifeLUNG will apply advanced machine learning and AI-driven deep sequencing to identify key immune factors and gene targets
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fine-tune machine learning and deep learning models to extract meaningful patterns and predict metastatic behavior Collaborate closely with experimentalists for mechanistic dissection of multimodal