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About the Role
We are seeking a Hybrid Algorithm Researcher to lead low-TRL research and development in Advanced Signal Processing (ASP) and Position, Navigation & Timing (PNT). This role involves fusing deep mathematics with modern AI to pioneer the next generation of algorithmic software for contested electromagnetic environments (CEME). You will bridge the gap between white-box mathematical rigor and black-box AI adaptability to build robust, explainable, and fast solutions.
Responsibilities
- Apply Physics-Informed Machine Learning (PIML) and hybrid architectures to complex sensor modelling, data fusion, and target tracking.
- Design algorithms engineered to survive and adapt in heavily jammed, spoofed, and contested electronic environments.
- Evaluate cutting-edge academic research, replicate novel techniques, and rapidly adapt traditional ASP/PNT algorithms for challenging, real-world data.
- Act as a Project Design Authority (PDA), contribute to high-value R&D bids, and mentor a high-innovation team of junior engineers.
Requirements
- Proven experience implementing hybrid algorithms (classical math + ML) for state estimation, anomaly detection, or information fusion.
- Deep familiarity with time/frequency domains, Fourier transforms, digital sampling, and filter design.
- Practical knowledge of EKF, UKF, Lie Group UKF, or Physics-Informed ML is highly advantageous.
- Fluency across C++, MATLAB/Mathematica, and Python.
- Bachelors, Masters, or PhD in a relevant STEM discipline, paired with a few years of recent experience in low-TRL ASP/PNT research.
- Requires Security Clearance (SC). Applicant must undergo, achieve, and maintain SC Clearance prior to employment.
- To be eligible for full SC, you 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
- Deep mathematics
- Modern AI
- Algorithmic software
- Advanced Signal Processing (ASP)
- Position, Navigation & Timing (PNT)
- Physics-Informed Machine Learning (PIML)
- Hybrid architectures
- Sensor modelling
- Data fusion
- Target tracking
- Classical physics
- Machine learning
- Robust algorithms
- Explainable algorithms
- Fast algorithms
- Time/frequency domains
- Fourier transforms
- Digital sampling
- Filter design
- EKF
- UKF
- Lie Group UKF
- C++
- MATLAB/Mathematica
- Python
Location
- Reading, United Kingdom
Work Type
- Hybrid
Experience Level
- Few years of recent experience in low-TRL ASP/PNT research
Education Level
- Bachelors
- Masters
- PhD
Benefits
- Performance-related bonus
- Half day every Friday, usually finishing around 13:00
- 28 days annual leave (plus bank holidays) with 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 and 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.