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PhD Aviation Research Intern

Cignus Consulting
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About Cignus

Cignus Consulting is an aviation technology and consulting firm supporting the FAA, NASA, DoD, TSA, and major airport authorities. We are a small, technically dense team — interns work directly on live research problems rather than side projects.

Position Summary

We are seeking a PhD student to support applied AI/ML research in two connected areas: (1) speech and language modeling for air traffic control voice communications, and (2) machine learning models for operational prediction in the airport and terminal environment. The role is research-oriented, hands-on, and expected to produce artifacts that feed directly into our platform and client deliverables.

What You'll Work On

  • Development and evaluation of automatic speech recognition (ASR) pipelines for ATC voice audio, including handling of phraseology, callsign structure, accent variation, radio artifacts, and low signal-to-noise conditions
  • Domain-adapted language and context models for interpreting controller-pilot exchanges — intent extraction, entity resolution (callsigns, runways, taxiways, fixes, clearances), and correction of ASR output against operational context
  • Integration of voice-derived data with surveillance and operational sources (ASDE-X/SWIM, ADS-B, NOTAMs, weather, airport geospatial data)
  • Development, training, and validation of predictive models for operational outcomes such as taxi routing and taxi time, surface delay, ground delay program onset, and weather-driven capacity constraints
  • Model evaluation and error analysis: benchmarking, ablation studies, uncertainty characterization, and documentation of results to a standard suitable for NASA/FAA technical review
  • Contributing to technical documentation, conference papers, or client-facing research briefings as opportunities arise

Required Qualifications

  • Currently enrolled in a PhD program in Aerospace Engineering, Computer Science, Electrical Engineering, Systems Engineering, Data Science, or a closely related field
  • Demonstrated coursework or research experience in machine learning, with practical experience in at least one of: speech recognition/audio ML, natural language processing, or time-series/sequence prediction
  • Strong Python proficiency and working experience with modern ML frameworks (PyTorch, TensorFlow, Hugging Face Transformers, or equivalent)
  • Ability to work with messy, real-world operational data — cleaning, alignment, labeling, and feature construction
  • Clear technical writing and the ability to explain methods and results to non-specialist stakeholders

Preferred Qualifications

  • Background in or working knowledge of aviation operations — air traffic control, airport surface operations, terminal airspace, or ATM concepts
  • Published or in-progress research in ATC speech recognition, aviation NLP, ATM prediction, or related areas (ATC ASR corpora such as ATCO2, ATCOSIM, LDC ATCC, or comparable datasets)
  • Experience with fine-tuning ASR models (Whisper, wav2vec 2.0, Conformer-based architectures) or with domain-specific language model adaptation
  • Familiarity with aviation data sources: SWIM, ASDE-X, ADS-B, TFMS, ASPM, METAR/TAF, NOTAMs
  • Exposure to simulation, digital twin, reinforcement learning, or multi-agent systems
  • Experience with cloud ML workflows (AWS SageMaker/Bedrock or Azure ML), Docker, and Git-based collaborative development