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& Responsibilities Developing automated manufacturability analysis methods for additive and conventional manufacturing processes. Applying machine learning techniques to 3D engineering data (CAD models, meshes, voxels
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Python, MATLAB, or C++. Experience with machine learning techniques, CAD, computational modeling, 3D printing, motion capture, and/or material testing. Proficiency in programming languages commonly used in
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custom tools and end effectors for robotic assemblyGood knowledge of the CAD software Rhinoceros 3D, its plugin Grasshopper 3D, and its API RhinoCommonReal-world architecture and/or construction
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using photolithography, e-beam lithography, and plasma etching. Experience in various 2D CAD drawings software e.g., KLayout, Layout editor, AutoCAD, etc. Experience in thin film deposition techniques
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salt breeders) is a plus. Experience in the analysis of heat transfer systems. Working knowledge of computer languages such as C++, FORTRAN, or Python. Experience with computer aided design (CAD
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acoustic ultrasound characterization knowledge a must. Candidates will need programming skills in MATLAB and CAD software skills. The ability to communicate scientific results to both broad and expert
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collaboratively in a multidisciplinary team Additional Experience (Preferred but Not Required): Experience in electrochemistry or micro/nanofabrication Prototyping skills, including CAD design, 3D printing
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disease (CAD). You will apply expertise in data science, machine learning, as well as multi-omics integration to predict and validate functional regulatory networks in vascular cell types. This work will
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needs. Tasks may involve CAD design, machining, and integration of sensors and control systems. Collaboration and Mentorship 5% Work collaboratively with faculty, graduate students, and other postdoctoral
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interpersonal skills and ability to work collaboratively with others Project management experience Experience with CFD and CAD software Understanding of sCO2, H2 and NH3 process and energy generation systems (i.e