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and industrial project partners. You will be responsible for: Developing and adapting machine-learning approaches for structure-based and generative molecular design. Integrating physicochemical
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learning (RL). 2) The Automation Group: Experts in designing and deploying custom robotic systems engineered for high-throughput sample synthesis, processing, and analytical characterization. This role is
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Saint Louis University is a Catholic Jesuit Institution dedicated to student learning, research, health care and service. This Postdoctoral Fellow position is in the medicinal chemistry research group led
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independent scholarship. Expectations include: • Serving as a key strategic partner to initiative leadership on cross-sector data strategy, measurement, evaluation, and learning activities. • Leading the design
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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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that learn multiple-state adiabatic potential-energy surfaces, energies, gradients or forces, derivative nonadiabatic couplings, and state-transition behavior from electronic-structure data. Design reliable
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design within an active, growing translational research program. This is an unusually opportune time to join the lab: the IBC work sits at the leading edge of a paradigm shift in how recurrent UTI is
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Job Description Our Commitment Texas A&M University is committed to enriching the learning and working environment by promoting a culture that respects all perspectives, talents & lived experiences
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
Posting Description POSTDOCTORAL ASSOCIATE, KEATING LAB, Biology, to join a small, collaborative peptide design team working at the interface of computation and experiment. Working closely with
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learning models (e.g., multimodal AI, large language/world models) with specific finetuning for ELEVATE; designing geographically context-sensitive urban design recommendations that promote active mobility