2026 PhD Residency - Non-Linear Physical Dynamics & Device Characterization (Future of Compute)

Project Goal:This is the flagship moonshot for 'The Future of Compute' at X (the Moonshot Factory). Our objective is to move away from the traditional paradigm of simulating physics on digital chips. Instead, we are building physical machines whose intrinsic dynamics ARE the computation itself, achieving a 1,000,000x improvement in useful compute per Joule.This residency focuses on the raw physics of non-linear computing. By studying how nanoscale devices behave, synchronize, and drift in a laboratory environment, you will extract the physical laws that make our substrate inherently superior to passive systems. You will study how to harness non-linear dynamics and physical noise to build stable, room-temperature probabilistic computers, bridging microscopic device physics with high-level circuit design.How you will make 10x impact: Collaborate on the physical modeling and physical characterization of nanoscale devices (e.g., RRAM, carbon nanotube FETs, and phase-locked micro-oscillators), focusing on mapping their non-linear phase-locking dynamics and non-equilibrium thermodynamics.Independently design and execute laboratory testing routines to characterize device-level non-linear activation functions, evaluating their suitability for physical neural network computation.Perform high-resolution noise-spectroscopy and time-domain measurements to analyze 1/f noise, thermal fluctuations, and resistance drift under long-term continuous operation.Analyze and model how ambient physical noise and stochastic thermal fluctuations can be harnessed as a computational resource for probabilistic computing (p-bits) and optimization, rather than suppressed.Investigate the physical coupling and synchronization dynamics of small arrays of physical oscillators, characterizing the limits of multi-phase locking and synchronization stability.Develop compact device behavioral models (e.g., Verilog-A, analytical Python equations) derived directly from physical measurements to update our circuit and system-level simulators.This project aims to push the limits of science and modeling as we know them and to prove how ML can radically accelerate our understanding of the worldLocation: X's headquarters in Mountain View, CAStart Date(s): Year-round rolling basisDuration: a flexible 4 mo. to 1 year program based on project team needs and your availabilityThroughout your AI Residency you can expect:To be embedded in an agile, confidential project team focused on physical exploration, challenging existing assumptions about noise, stability, and digital over-engineering.Direct mentorship from experimental device physicists, materials scientists, and advanced measurement engineers.Access to state-of-the-art semiconductor characterization labs and device probing equipment.What you should have:Currently enrolled in a PhD program in Physics, Applied Physics, Materials Science, Electrical Engineering (solid-state electronics focus), or a related STEM field.Strong hands-on experience in the physical and electrical characterization of nanoscale devices (such as memristors/RRAM, nanoscale oscillators, or 2D materials) in a laboratory environment.Proficiency in scripting automated measurements (Python/PyVISA, LabVIEW) and analyzing complex physical and time-series datasets.Deep theoretical understanding of solid-state device physics, charge transport, noise processes, and non-equilibrium statistical mechanics.Ability to translate physical device behavior into analytical, numerical, or compact models (e.g., Python, MATLAB, COMSOL, or Verilog-A).It’d be great if you also had these:Prior experience characterizing phase-locked coupled-oscillator networks, RF micro-oscillators, or stochastic/probabilistic circuits (p-bits).Familiarity with carbon nanotube electronics, novel non-volatile memories, or advanced atomic-force microscopy (AFM/conductive-AFM).Additional public information:The US base salary range for this position is $100,000 - $147,000 + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your location during the hiring process.Please note that the compensation details listed in US role postings reflect the base salary only, and do not include benefits.