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with stakeholders. QUALIFICATIONS Doctoral degree in Computer Science, Electrical/Computer Engineering, or a closely related field. Foundation in network security for 5G/6G and O-RAN, including TLS 1.2
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molecular systems. Method development, potentially in the realm of time-dependent current DFT, will likely also be required as we learn about physical phenomena underlying the CISS effect. Bhat group
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causal inference, machine learning, and artificial intelligence is desirable ● Experience with clinical, EHR, or biobank data analyses is desirable Application Instructions To apply: Interested
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volcanism. Physical oceanography: analysis of surface gravity waves, infragravity waves, internal tides, and vortex-induced vibrations for estimating ocean current speeds. Acoustic ecology and ship noise
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APPLICATION INSTRUCTIONS: CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process . Please do not apply
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APPLICATION INSTRUCTIONS: CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process . Please do not apply
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of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning
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collected through ongoing SCEC programs. This data will support the postdoc's work developing and deploying multimodal models to improve the iFIND tool, extending its current text-based approach. Primary
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Mechanical, Aerospace or Electrical Engineering, or other related fields specializing in the area of machine learning and dynamical modeling, excellent problem-solving skills as well as good oral and written
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. Qualifications and Requirements: Must have Ph.D. by the start of the appointment in Electrical/Computer/Mechanical/Materials/Nuclear Engineering or a related discipline. Must be a US citizen. Must pass the Sandia