PhD Position in Event-Driven GNSS Processing

Offer Description

Would you like a job in which your talent really has an impact on your surroundings? The UOC is not simply a university: our institution is dynamic, determined and ambitious, and drives social and educational changes. Over 30 years ago, we made history as the world's first online university. Our goal is to generate, transfer and join together knowledge to build a more critical, responsible and non-conforming society.

We are seeking to recruit a Postdoctoral Researcher for UOC-TECH - Research Centre in Ethical Technologies and Connectivity for Humanity at UOC, within the Wireless Networks (WiNe) research group.

WiNe is a young and highly motivated research team working on experimental wireless and networking systems, with a strong focus on translating fundamental research into working prototypes and real-world technologies.

You will work at the intersection of:

• GNSS and satellite navigation
• Statistical signal processing and estimation
• RF and receiver design
• Event-based and neuromorphic computing
• Analog and mixed-signal processing
• Embedded and low-power systems
• Experimental wireless systems

The research environment combines theoretical research, software development, SDR experimentation, and hardware prototyping, providing opportunities to take ideas from mathematical models through to experimental systems.

Research Project

Global Navigation Satellite System (GNSS) receivers traditionally rely on continuous, high-rate digital sampling followed by signal acquisition, correlation, synchronization, tracking, and navigation algorithms. While these architectures provide excellent performance, they can require substantial computation, data movement, and energy, particularly in battery-powered and resource-constrained devices.

This PhD project investigates a fundamentally different approach: event-driven GNSS processing.

Instead of continuously sampling and processing the GNSS waveform at a high rate, the receiver will generate and process sparse, information-bearing events that preserve the signal characteristics needed for synchronization, parameter estimation, and Position, Velocity and Time (PVT) computation.

The central scientific question is: How much of the information contained in a GNSS waveform needs to be represented and processed to achieve a given positioning performance, and can this information be extracted and processed as sparse asynchronous events rather than conventional high-rate digital samples?

The project will investigate the theoretical foundations, algorithms, architectures, and experimental implementation of such receivers. The research will span the processing chain, from RF signal acquisition and signal representation to synchronization, tracking, statistical inference, and navigation.

The central hypothesis is that appropriately designed event-driven representations can substantially reduce data movement, computation, and energy consumption while maintaining the accuracy, robustness, and availability required for practical GNSS positioning.

The project combines statistical signal processing, estimation theory, GNSS receiver design, event-based sensing, analog and mixed-signal processing, neuromorphic computing, and low-power embedded systems.

The project aims to establish a scientific and experimental foundation for a new class of ultra-low-power GNSS receivers.

Expected outcomes include:

• New mathematical models for event-driven GNSS signal representations.
• Novel signal-processing, estimation, and/or machine-learning algorithms.
• Experimental implementations and proof-of-concept receiver architectures.
• Quantitative comparisons with conventional GNSS processing.
• Scientific publications in leading international conferences and journals.
• Contributions to open-source software, datasets, or experimental platforms where appropriate.
• A doctoral thesis establishing new knowledge in event-driven GNSS processing.
• Opportunities for collaboration with international academic and industrial partners.

Responsibilities

The predoctoral researcher will investigate several of the following research directions:

• Develop mathematical models for sparse and event-driven representations of GNSS signals and their relationship to conventional sampled representations.
• Investigate event-driven methods for GNSS signal acquisition, synchronization, and tracking, including code delay, Doppler frequency, and carrier-phase estimation.
• Develop statistical inference and filtering methods operating directly on asynchronous event streams.
• Study how event timing, amplitude, phase, and other event-derived observables can be exploited for positioning and navigation.
• Develop robust PVT estimation methods from sparse and asynchronous measurements.
• Investigate learning-based approaches for event generation, signal representation, acquisition, tracking, and estimation where appropriate.
• Explore mappings of event-driven GNSS algorithms onto analog, mixed-signal, neuromorphic, or other low-power computing architectures.
• Implement algorithms in simulation and on software-defined radio (SDR) platforms, followed where appropriate by hardware prototypes.
• Characterize the trade-offs between positioning accuracy, robustness, latency, computational complexity, data rate, and energy consumption.
• Benchmark event-driven receiver architectures against conventional GNSS processing under realistic signal and channel conditions.
• Contribute to experimental demonstrations, scientific publications, and potentially open-source research artifacts.

The successful candidate will have the opportunity to work across the boundary between theory and experimental systems, developing new mathematical models and algorithms and validating them using software-defined radio and hardware prototypes.

The research workflow may span: Theory → Signal Representation → Algorithms → Simulation → SDR → Hardware → Experimental Validation.

The precise balance between these activities will evolve throughout the PhD according to the research direction and the candidate's expertise and interests.

Depending on the research direction, your work may involve a combination of these development phases, such as transforming conventional GNSS signal information into sparse events, deriving estimators that operate on those events, implementing the resulting algorithms on an SDR platform, and experimentally evaluating the resulting trade-offs in positioning accuracy, robustness, data-rate, and energy.

Other possible research directions include learning-based event representations, GNSS acquisition and tracking, asynchronous statistical inference, analog or mixed-signal implementation, neuromorphic processing, or ultra-low-power embedded architectures.

The exact research direction will be refined during the PhD based on the scientific questions that emerge, the candidate's background, and the team's expertise.

The predoctoral contract will be full-time for three years. The start date is January 2027.

The position is categorised as that of a predoctoral researcher in training. The selected person must formalise and submit their enrolment in the doctoral programme prior to the start of the contract.

This position offers the opportunity to investigate a potentially new class of GNSS receiver architectures rather than incrementally optimizing conventional receiver designs.

You will have the opportunity to explore fundamental questions about what information a GNSS receiver actually needs to process, how that information can be represented as sparse events, and how new inference algorithms and hardware architectures can exploit those representations.

The project is particularly suited to candidates who enjoy both theoretical and experimental research and who want to develop expertise across signal processing, wireless systems, machine learning, and emerging low-power computing architectures.

The goal is not only to develop algorithms, but to take promising ideas through simulation, implementation, and experimental validation.

Where to apply

Website: https://selection.uoc.edu/web/profile/login.aspx

Requirements

• Research Field: Engineering; Education Level: Master Degree or equivalent
• Research Field: Computer science; Education Level: Master Degree or equivalent
• Research Field: Physics; Education Level: Master Degree or equivalent
• Research Field: Mathematics; Education Level: Master Degree or equivalent

Specific Requirements

Candidate profile

• Education: Master's degree (or equivalent) in one of the following areas or a closely related field: Electrical or Electronic Engineering, Telecommunications, Signal Processing, Computer Engineering, Applied Mathematics, Control or Systems Engineering, Robotics, Physics, Computer Science with a strong background in signal processing or systems. Candidates with strong foundations in one or more relevant areas are encouraged to apply even if they do not have previous experience across the entire GNSS receiver chain.
• Strong technical foundation and a genuine interest in conducting interdisciplinary research at the intersection of signal processing, wireless systems, estimation, and low-power computing. Particularly relevant competencies include:
  • Fundamentals of signal processing and stochastic modelling.
  • Probability, statistics, and mathematical modelling.
  • Basic knowledge of estimation, inference, or filtering.
  • Understanding of communication or wireless systems.
  • Ability to formulate technical problems and investigate them systematically.
  • Interest in developing new algorithms and validating them experimentally.
  • Strong analytical and problem-solving skills.
  • Ability to work independently while collaborating with researchers from different technical backgrounds.
  • Motivation to develop expertise beyond the candidate's initial area of specialization.
• A strong foundation and research potential are more important than having prior expertise in every aspect of the project.
• Relevant experience or coursework may include:
  • GNSS signal structures and receiver architectures.
  • GNSS acquisition and tracking.
  • Delay-locked loops (DLL), phase-locked loops (PLL), or frequency-locked loops (FLL).
  • Pseudorange, carrier phase, and Doppler measurements.
  • Positioning, navigation, and PVT estimation.
  • RF and communication systems.
  • Analog or mixed-signal electronics.
  • Event-based sensing and asynchronous signal processing.
  • Neuromorphic computing or spiking neural networks.
  • Machine learning or deep learning for signal processing.
  • FPGA, ASIC, or embedded systems.
  • Low-power computing and hardware/software co-design.
• Knowledge of all of these areas is not expected. The PhD is intentionally interdisciplinary, and part of the doctoral training will involve developing expertise in areas beyond the candidate's initial background.
• Specific knowledge: Strong programming and numerical-computing experience in one or more of the following areas:
  • Python, MATLAB, and/or C/C++.
  • Numerical simulation and data analysis.
  • Signal-processing implementation.
  • Scientific computing and algorithm development.

Also of interest

• Although knowledge across all areas is not expected, experience in the following will be positively assessed: GNSS signal structures, software-defined radio (SDR), Linux and embedded systems, FPGA development and hardware/software co-design.
• Knowledge in laboratory instrumentation and data acquisition, RF or analogue/mixed-signal systems, and computationally efficient or low-power implementations.
• Motivation to develop expertise beyond the initial specialisation area.

Languages: ENGLISH Level: Excellent

Research Field: Engineering, Computer science, Physics, Mathematics

Years of Research Experience: None

Additional Information

Benefits

The predoctoral contract will be full-time for three years. The start date is January 2027.

The position is categorised as that of a predoctoral researcher in training. The selected person must formalise and submit their enrolment in the doctoral programme prior to the start of the contract.

Reasons to choose the UOC

• Shared purpose: you'll be working for and championing access to quality higher education that enables people to transform their lives.
• Culture of values: we're driven by excellence, passion, non-conformity, kindness and commitment, but what makes us unique is the people who comprise the UOC. We value positive attitudes and teamwork, we care for talent and we promote a working environment based on respect and relatability.
• Continual innovation: you'll be joining a university with an innovative, people-centred educational model, and a hub of impactful, interdisciplinary applied research.
• Real impact: you'll be part of a community that is driven by its mission, where it will be possible for your work to directly make a difference for society.

Benefits

• Friday afternoons off.
• Flexibility: our open working model combines remote working with work on-site, depending on organizational needs and the nature of the tasks involved.
• Each year you'll have 37 days of paid time off (24 days of vacation, 9 personal leave days and 4 UOC holidays). And you'll be able to work reduced summer hours (35 hours a week), subject to organizational requirements.
• You'll be provided with the computer equipment and ergonomic material you need to work both from home and on site at the UOC.
• Training: professional development training is provided regularly.
• Well-being: medical service, psychological help service, physiotherapy service, and health promotion activities.

Selection process

Application

To take part in the selection process, you must register as a candidate in the Join our team section of our website (www.uoc.edu). Once you have registered, complete your profile in the candidate section in order to submit your application.

Please send your CV, academic records, and a motivation letter to:

• Prof. Xavier Vilajosana — xvilajosana@uoc.edu
• Dr. Guillem Boquet — gboquet@uoc.edu

In your motivation letter, please briefly describe:

• Your academic background and most relevant projects, research, or professional experience.
• Which aspects of the candidate profile and technical requirements you believe you match, and provide concrete evidence of this match by referring to relevant coursework, research projects, thesis work, publications, internships, professional experience, or other technical work.
• The technical areas in which you could contribute to the project.
• Any experience with signal processing, GNSS, SDR, RF/analog hardware, embedded systems, machine learning, or event-driven/neuromorphic computing.
• What aspects of the project particularly interest you.
• Why you would like to pursue a PhD in this research area.

Candidates are encouraged to apply even if they do not match every item in the preferred-knowledge list. We are particularly interested in candidates who bring strong fundamentals, research curiosity, and the motivation to develop interdisciplinary expertise.

When describing your fit, please focus on specific evidence rather than simply listing skills. For example, if you have experience in signal processing, explain where and how you developed and applied that experience through a course, thesis, research project, publication, software implementation, or other relevant work.

We welcome applications from candidates with strong foundations and interests in one or more of the following areas:

• Statistical signal processing.
• Estimation and inference.
• Wireless communications.
• GNSS and satellite navigation.
• RF and analog/mixed-signal systems.
• Event-based sensing.
• Neuromorphic or asynchronous computing.
• Machine learning for signal processing.
• Embedded and low-power systems.
• Signal-processing hardware and hardware/software co-design.

We are particularly interested in candidates who combine strong fundamentals, curiosity, and the motivation to work across disciplinary boundaries.

For example, a candidate with a strong background in statistical signal processing but limited GNSS experience may be an excellent fit. Similarly, candidates coming from GNSS, wireless communications, embedded systems, analog hardware, machine learning, or neuromorphic computing are encouraged to apply.

Prior GNSS experience is therefore valuable but not a prerequisite.

Additional comments

Our commitment to diversity

At the UOC, our differences make us stronger. We want recruit the best talent while guaranteeing equal opportunities, without discrimination relating to gender, age, ethnicity, sexual orientation or any other personal trait. Feel free to contact our recruitment staff for more information.

Our recruitment and selection processes are based on the principles of Open, Transparent and Merit-based Recruitment (OTM-R), and we have earned the Human Resources Strategy for Researchers (HRS4R) Excellence in Research award.

Project PID2025-169739OB-C33 funded by MICIU/AEI/10.13039/501100011033.

Website for additional job details: https://selection.uoc.edu/web/offersjob/offerdetails.aspx?offerID=8BDC2A147FE6F…

Work Location(s)

• Number of offers available: 1
• Company/Institute: Universitat Oberta de Catalunya
• Country: Spain
• State/Province: Barcelona
• City: Barcelona
• Postal Code: 08018
• Street: Rambla del Poblenou, 156

Contact

• State/Province: Barcelona
• City: Barcelona
• Website: https://www.uoc.edu/portal/en/uneix-equip/ofertes/index.html
• Street: Rambla Poble Nou, 154-156
• Postal Code: 08018
• E-Mail: hrstrategy@uoc.edu