ML and signal intelligence for critical infrastructure timing trust - ON-1249
Genre de projet: RechercheDiscipline(s) souhaitée(s): Génie - informatique / électrique, Génie, Informatique, Sciences mathématiques
Entreprise: Alvian Networks
Durée du projet: Plus d’un an
Date souhaitée de début: 09/22/2026
Langue exigée: Anglais
Emplacement(s): ON, Canada
Nombre de postes: 3
Niveau de scolarité désiré: CollègeÉtudes de premier cycle/baccalauréatMaîtriseDoctorat
Ouvert aux candidatures de personnes inscrites à un établissement à l’extérieur du Canada: No
Au sujet de l’entreprise:
Alvian Networks is an Ontario-based deep-tech company building the trust layer for critical infrastructure. Our platform, the Alvian Trust Stack, independently validates timing integrity, detects GNSS spoofing and jamming, and produces cryptographically signed, tamper-evident event records for electric utilities, telecom operators, and government and defense customers across North America and Europe. The platform combines purpose-built edge hardware (the Alvian Edge Node), cloud monitoring (Alvian Sentinel), and on-premises software (Alvian Sentinel+). Founded in 2025 by an experienced team with a prior successful exit in Canadian telecommunications.
Veuillez décrire le projet.:
Alvian Networks is developing the Alvian Edge Node (Patent Pending), a purpose-built hardware platform for timing integrity validation, GNSS threat detection, and power quality monitoring in critical infrastructure. This 12-month applied research program addresses three connected research problems that are architecturally defined but require graduate-level expertise to formalize, implement, and validate. Research Area 1: Cross-site event correlation and fault localization using high-precision timing data normally unavailable to standard monitoring systems. Research Area 2: Local RF environment fingerprinting and anomaly detection for stationary GNSS receivers using ML, building learned baselines of the local RF environment to detect degradation, interference, and spoofing activity. This approach is novel because existing GNSS research focuses on mobile platforms; stationary infrastructure receivers can learn a stable RF fingerprint over time. Research Area 3: Power quality waveform classification and disturbance propagation analysis using high-resolution ADC data and nanosecond-accurate timestamps to exceed the capabilities of standard reference measurement devices such as the ADE9000. Each research area will be addressed by a dedicated intern across a 4 to 6 month term, sequenced through 2027. All IP generated remains with Alvian Networks. The goal of the company is a certified, commercialized AEN platform deployed across North American and European critical infrastructure.
Expertise ou compétences exigées:
Research Area 1: Signal processing, statistical inference, distributed systems, power systems fundamentals, TDOA correlation methods. Research Area 2: Machine learning, GNSS receiver theory, RF signal propagation, signal processing, anomaly detection algorithms. Research Area 3: Digital signal processing, machine learning, power systems, IEC 61000 standards, high-speed ADC data analysis. Asset for all areas: Python or C++ programming, embedded systems familiarity, interest in critical infrastructure cybersecurity.
