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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
. The work will apply state-of-the-art three-dimensional atmospheric chemistry and circulation models, together with advanced statistical techniques (optimal Bayesian, Markov Chain-MonteCarlo, etc.) to solve
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decision support systems. The candidate would also assist in writing python/R programming scripts to optimize processing time, making the current decision support systems more efficient and reliable
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optimization under uncertainty, constrained query evaluation, or the design of efficient, explainable, and scalable query engines. The successful applicant will help design and build novel systems and algorithms
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qualified Postdoctoral Research Associate. The incumbent will join a collaborative team to combine synthetic magnetosome biogenesis and computationally optimized cobalt-binding peptides to enable efficient
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., model-based, optimal, learning-augmented control), perception, and planning modules; run structured experiments and benchmarking. Architect high-quality research codebases in C++/Python/C# (e.g., ROS/ROS
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to world-class facilities and equipment. Key Responsibilities: Formulate and optimize nanoparticle platforms using techniques such as microfluidics, extrusion, and self-assembly. Perform comprehensive
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, Skills, and Abilities Candidates must be able to: Design retro-synthesis routes for screening hits and perform structure-activityrelationship studies Optimize the in vitro and in vivo pharmacokinetic
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opportunity for someone who thrives in an interesting and challenging work environment. Core responsibilities include: Research in the intersection of discrete optimization and machine learning, including
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Deploy, commission, and operate TES X-ray microcalorimeter spectrometers at APS beamlines, including cryostat integration, beamline interfaces, alignment, and stability optimization. Perform pathfinding
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opportunity for someone who thrives in an interesting and challenging work environment. Core responsibilities include: Research in the intersection of discrete optimization and machine learning, including