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About the Role

Develop advanced algorithmic software for future products in Advanced Signal Processing (ASP) and Position, Navigation & Timing (PNT). This role involves working at the intersection of classical signal processing and AI-based methods to solve complex sensing, modelling, tracking, fusion, and estimation challenges in contested electromagnetic environments. Contribute to low-TRL research and development, shaping robust and adaptable technologies for real-world and adversarial conditions.

Responsibilities

Lead the development of hybrid algorithmic software in C++, MATLAB, Mathematica, and Python.
Design, adapt, and evaluate hybrid algorithms for sensor modelling, processing, data fusion, tracking, state estimation, and anomaly detection.
Apply time- and frequency-domain methods, including Fourier analysis, digital sampling, and filter design.
Review recent research and translate novel techniques into practical ASP/PNT solutions.
Improve the robustness of traditional algorithms for challenging, real-world data and systems.
Use machine learning where it adds value, particularly for non-linear dynamics and complex system behaviour.
Support proposals, bids, and project delivery for research and development activities.
Mentor junior engineers and help strengthen technical capability across the team.

Requirements

Substantial recent experience in low-TRL research and development, ideally within ASP, PNT, sensor fusion, or a related field.
Confidence working across theory and implementation.
Comfort applying hybrid white-box and black-box approaches to real engineering problems.
Experience implementing hybrid algorithms that combine classical and ML-based approaches.
Applied experience in one or more of the following: feature extraction, data fusion, target tracking, image segmentation, image matching, sensor calibration, state estimation, anomaly detection, integrity monitoring, system modelling, or synthetic data generation.
Strong understanding of time and frequency domain methods, including Fourier transforms, digital sampling, and filter design.
Ability to evaluate, adapt, and implement techniques from recent research papers.
Experience making algorithms robust to real-world data and complex physical system behaviour.
Practical coding experience in C++, MATLAB, Mathematica, and/or Python.
Proven ability to work across research, development, technical leadership, and delivery.
Knowledge of emerging sensing and PNT technologies, electronic warfare, radar, sonar, or related domains.
Experience with EKF, UKF, Lie Group UKF, or physics-informed machine learning.
Experience supporting bids, funding applications, or collaborative consortia.
Exposure to contested electromagnetic environments and resilient sensing challenges.
Experience mentoring engineers or acting in a technical leadership capacity.
Technically curious problem-solver who enjoys working at the boundary between research and practical engineering.
Ability to move confidently between mathematical reasoning, software development, and applied experimentation.
Ability to know when to use classical methods, machine learning, or a combination of both.
Ability to communicate complex ideas clearly.
Ability to work well across disciplines.
Motivation by difficult problems that have real operational value.
Pride in producing high-quality technical output and contributing to an innovative, collaborative environment.
Requires Security Clearance (SC). Applicant must undergo, achieve, and maintain SC Clearance prior to employment.
To be eligible for full SC, generally need to have resided in the UK for the last 5 years. In some circumstances, a minimum of 3 years’ residence in the UK over the last 5 years may be accepted, with additional overseas checks.

Skills

C++
MATLAB
Mathematica
Python
Advanced Signal Processing (ASP)
Position, Navigation & Timing (PNT)
Sensor Modelling
Data Fusion
Tracking
State Estimation
Anomaly Detection
Time-domain methods
Frequency-domain methods
Fourier analysis
Digital sampling
Filter design
Machine Learning
Classical signal processing
AI-based methods
Electronic warfare
Radar
Sonar
EKF
UKF
Lie Group UKF
Physics-informed machine learning

Location

Reading, United Kingdom

Work Type

Hybrid

Experience Level

Few years of recent experience in related research and development
Substantial recent experience in low-TRL research and development

Education Level

Bachelors degree with honours
Masters degree
PhD

Benefits

Performance-related bonus
Half day every Friday, usually finishing around 13:00
28 days annual leave (plus bank holidays)
Opportunity to buy up to 40 hours/year (pro rata)
24 hours volunteering paid for
Private healthcare
Pension scheme
Life cover
24/7 Employee Assistance Program
Access to mental wellbeing app
Employee discount shopping schemes on major brands and retailers
Gym membership discounts
Private medical insurance
Buying or selling annual leave
Cycle to work schemes
Employee discounts
Paid volunteering day
Stocks and shares
Annual bonus

About the Company

Thales is a global technology leader with more than 83,000 employees on five continents.
With over 7,500 people in the UK, operating across defence, space, aerospace, and digital security, we help build a future we can all trust.
Thales supports the security and stability of our nation by providing extraordinary technology to our customers, as well as delivering social value to the UK with our products and services.

Equal Opportunity

At Thales, we ensure equal opportunities, pay and working conditions for all.
We are committed to creating a workplace where everyone feels valued for who they are and the unique strengths they bring.