PhD Intern - Detection Physics

Pacific Northwest National Laboratory•UNAVAILABLE, UNAVAILABLE

1d•Onsite

About The Position

PNNL is seeking graduate level interns with a background in software analysis to support a primarily R&D focused program spanning large-scale simulation, device emulation, software analysis, and remediation. This role blends hands-on development with deep technical evaluation and offers the opportunity to contribute to technical writing and publication. Seeking an intern to support scientific research in the broad area of data science, machine intelligence and autonomous learning and reasoning. A successful candidate should have expertise in one or more of the following technical areas: optimization and optimization-based decision making, artificial intelligence and machine learning, and data analytics applied to modeling, diagnostics and control of cyber-physical systems, uncertainty quantification, system identification and model reduction, dynamical systems modeling and simulation, model predictive control, reinforcement learning algorithms, autonomous control and decision systems, and multi-agent systems. Specific research is required to expand library capability across other domains and content types (including text, video and audio content) including collaborative research in the continued development of the Python library.

Requirements

Currently enrolled/matriculated in a PhD program at an accredited college.
Minimum GPA of 3.0 is required.
U.S. Citizenship is required.

Nice To Haves

Experience with one or more disassemblers (IDA, Binary Ninja, Ghidra)
Experience with one or more assembly languages (x86, ARM, RISCV, etc)
Comfortable with software development in compiled and scripting languages (C, C++, Rust, Python, etc)
Experience with object files, compilers, linkers, and loaders (ELF, DWARF, GDB, LLVM)
Experience with both static and dynamic program analysis techniques
Experience with reverse engineering firmware and/or embedded systems
Experience with emulation frameworks such as QEMU
Experience with software exploitation techniques and mitigations
Experience with attack surface mapping across systems and system-of-systems
Experience with networking protocols and analysis thereof
Experience with automation of vulnerability detection
Experience with utilizing emerging artificial intelligence technologies for vulnerability detection or automation thereof
Comfortable utilizing hypervisors and containers (VMware, Proxmox, Docker, etc)
Experience with CTFs including challenge development
Hardware Device Enumeration
Prior research experience, including technical writing, reports, or peer-reviewed publication

Responsibilities

Research to expand library capability across other domains and content types (including text, video and audio content)
Collaborative research in the continued development of the Python library
Prepare data sets for model training
Test deep learning models to obtain accuracy
Research deep learning topics
Contribute to project deliverables including contributing to monthly reports using the standard report template
Participate in weekly teleconferences as scheduled by the PNNL PM/PI