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Field
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. Proficiency in computer programming, including scripting in Python, Fortran, or other computing tools for data processing and modeling. Experience in grant proposal writing within a collaborative environment
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Postdoctoral Associate to investigate the neural mechanisms underlying continual learning in humans. The successful candidate will develop computational models examining the tradeoff between task
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cardiovascular treatments. In this role you will apply both machine learning predictive modelling and human genetic analyses of non-coding regulatory sequences to identify cell-specific targets for coronary artery
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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Machine Learning Techniques for longitudinal data analysis. An understanding of class biases, model explainability (ex: Shaply), data augmentation techniques, missing data handling techniques, validation
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preferred. Proficiency in computational tools such as MATLAB, Python, R, or machine learning applications in immunology is desired. Candidates should have a PhD in Chemistry, Chemical Engineering
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genome-based influenza and/or SARS-CoV-2 risk assessment algorithms, designing broadly protective influenza and SARS-CoV-2 vaccines using Machine Learning and Artificial Intelligence, modeling the impacts
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-scale human health datasets (e.g., UKBioBank, WHI, All of Us) using statistical and artificial intelligence/machine learning techniques. Develop and implement machine learning models, including
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have expertise and experience in executing density functional theory (DFT) calculations, microkinetic modeling, kinetic Monte Carlo simulations, and Machine learning methods. Minimum Qualifications: Must
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Expert in advanced machine learning such as multi-agent generative AI, LLMs, Diffusion models, and traditional machine learning techniques Expert in CALPHAD-based ICME techniques Expert in combining