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candidate is also expected to play an active role in other projects and activities within the Smart Materials Lab and assist in supervising undergraduate or PhD students. Applicants must hold a PhD in
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, organization of scientific workshops, and attendance at conferences. Key qualifications include a PhD in a relevant field, expertise in AI/ML (e.g., PyTorch, TensorFlow, Python), interest in materials
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will create a personalized training and development plan with the supervisor. Minimum Qualifications Currently has or is in the process of completing a PhD, MD/PhD, DPhil or equivalent terminal degree
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learning theory to join the research team of Prof. Muhammad Umar B. Niazi. The position focuses on the design and implementation of incentive mechanisms for sociotechnical and cyber-physical-human systems
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agreement. The successful candidate is expected to take a leading role in the development of the group tasks and help in supervising PhD students. Applicants must have a PhD in experimental high energy
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models and data science ideas. Applicants must have received a PhD in engineering, computer science, urban science, or a related field. Experience in transportation, in particular related to urban science
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infrastructure design, management, and policymaking. Investigating the residual sources of PM2.5 exposure from transportation systems following electric vehicle (EV) adoption, including exposure due to emissions
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strategies. Design, fabricate, and characterize functional membranes (e.g., conductive, anti-fouling, electrochemical systems). Operate and evaluate membrane systems (RO, NF, UF) at lab and pilot scales. Apply
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infrastructure design, management, and policymaking. Investigating the residual sources of PM2.5 exposure from transportation systems following electric vehicle (EV) adoption, including exposure due to emissions
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applicant will lead an innovative project focused on the development of surface-patterned membranes with tailored surface features designed to enhance the performance of microfiltration (MF), ultrafiltration