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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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-based or machine-learning/AI-based climate modeling (e.g. hydrometeorological and/or atmospheric processes) are particularly encouraged to apply. Position 3 Working with Dr. Kelly Baker , EEH Associate
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Internal Number: JR89988 Position Summary This position will primarily support the implementation of a newly funded National Institute of Mental Health R01 grant focused on optimizing a digital chatbot
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from telescope data. The design of robust uncertainty quantification tools is a core component of this effort. -On the experiment design side, the group develops simulation and optimization algorithms
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…). Familiarity with scaling up biotechnological processes and designing pilot-scale plants. Ability to analyze experimental data, perform mass/energy balances, and optimize process conditions. Proven experience in
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(FPPM) RDI unit. The FPPM unit addresses the synthesis, formulation and processing of polymers and (nano)particles to achieve specific properties enhancements and multi-functionality through both
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 1 month ago
and papers supporting the scientific case for polarized deuterons. Contribute to the design, simulation, and performance optimization of the BHCal for the EIC. Participate in the assembly, calibration
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. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in mechanical engineering, industrial engineering
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optimize the synthetic genome that encodes for a biological function such as shape manipulation. Next to all this, the candidate will also do research on entropic segregation of synthetic genomes by testing
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of the required technology and culture procedures: Stereolithographic 3D printing of complex microfluidic networks in hydrogel materials. Optimizing the hydrogel composition to maximize diffusional transport