Postdoctoral Researcher

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

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 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 complete 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 and signal-processing methods for event-driven GNSS.
• Novel acquisition, tracking, and estimation algorithms.
• Experimental receiver prototypes and proof-of-concept implementations.
• Quantitative comparisons with conventional GNSS receiver architectures.
• Publications in leading international conferences and journals.
• Open-source software, datasets, or experimental artifacts where appropriate.
• Potential collaboration with industrial and technology partners working on low-power wireless and positioning systems.

Responsibilities

The postdoctoral 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 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 and publications in GNSS, statistical signal processing, wireless systems, embedded systems, and low-power computing.

Where to apply

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

Requirements

Research Field

• Engineering » Communication engineering — PhD or equivalent
• Engineering » Computer engineering — PhD or equivalent
• Engineering » Control engineering — PhD or equivalent
• Engineering » Electrical engineering — PhD or equivalent
• Engineering » Electronic engineering — PhD or equivalent
• Engineering » Systems engineering — PhD or equivalent
• Computer science — PhD or equivalent
• Mathematics » Applied mathematics — PhD or equivalent

Specific Requirements

Candidate profile

• Education: PhD degree in Electrical, Electronic, Telecommunications Engineering, Signal Processing, Computer Engineering, Applied Mathematics, Control or Systems Engineering, Robotics or a related field involving estimation and sensing. People with strong experience in one or more of these areas are encouraged to apply, even if their background does not cover the entire GNSS receiver chain.
• Experience: Solid research experience in signal processing, estimation, wireless/RF systems, or low-power sensing, together with the ability to move between mathematical modelling, algorithm development, and experimental validation. Particularly relevant competencies include:
  • Statistical signal processing and stochastic modelling.
  • Estimation theory, Bayesian inference, or state-space modelling.
  • Signal detection, synchronization, parameter estimation, and filtering.
  • Digital or analog communication systems and receiver architectures.
  • Mathematical modelling and analysis of noisy measurement systems.
  • Algorithm design, simulation, and quantitative performance evaluation.
  • Ability to formulate research hypotheses, design experiments, and interpret experimental results.
  • Interest in interdisciplinary research spanning algorithms, circuits, embedded systems, and sensing technologies.
  • Ability to work independently while collaborating across different technical disciplines.
• Specific knowledge: Strong programming and numerical-computing skills are expected in at least some of the following:
  • Python, MATLAB, and/or C/C++ for algorithm development, simulation, and data analysis.
  • Numerical simulation and statistical analysis of communication or sensing systems.
  • Signal-processing implementation and optimization.
  • Experience with software-defined radio (SDR) platforms and/or real-time signal-processing systems is highly desirable.
  • Experience with Linux, embedded systems, FPGA development, or hardware/software co-design is advantageous.
  • Experience with instrumentation, data acquisition, laboratory measurement, and experimental prototyping is advantageous.
  • Experience implementing computationally efficient or low-power algorithms is a plus.
• Languages:
  • Oral and written communication skills in both Catalan and Spanish.
  • English language skills to at least CEFR level B2, or First Certificate equivalent.

Also of interest

• Prior knowledge of GNSS is valuable but not mandatory for candidates with strong relevant expertise in signal processing, estimation, communications, or sensing. Particularly relevant experience includes:
  • GNSS signal structures, acquisition, synchronization, and tracking.
  • Delay-locked loops (DLL), phase-locked loops (PLL), frequency-locked loops (FLL), and related estimation techniques.
  • GNSS observables, including pseudorange, carrier phase, Doppler, and carrier-to-noise density ratio.
  • GNSS positioning, navigation, and PVT estimation.
  • RF front-end and receiver architectures.
  • Analog, mixed-signal, or low-power circuit design.
  • Event-based sensing, asynchronous signal processing, neuromorphic computing, or spiking/event-driven systems.
  • FPGA, ASIC, analog computing, or mixed-signal implementation.
  • Hardware/software co-design and energy-efficient embedded architectures.
• Knowledge of all of these areas is not expected. The project is intentionally interdisciplinary, and candidates with a strong profile in one or more of the core research areas are encouraged to apply.

Languages: ENGLISH

Level: Excellent

Research Field: Engineering » Computer engineering; Engineering » Computer engineering; Engineering » Electrical engineering; Engineering » Electronic engineering; Computer science

Years of Research Experience: 1 - 4

Additional Information

Benefits

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 depend on the candidate's expertise and interests.

Depending on your background and interests, 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 research directions may focus more strongly on statistical inference, GNSS acquisition and tracking, analog or mixed-signal implementation, neuromorphic processing, or low-power embedded architectures. The precise research direction will be developed jointly with the research team based on the candidate's expertise and interests.

The contract will be fixed-term until 2 years and full-time. We will agree upon a start date once the recruitment process is completed.

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 freedom 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 such representations.

The project is particularly suited to researchers who enjoy moving between theory and experiments, and who want to work on a research problem spanning signal processing, wireless systems, and emerging low-power computing architectures.

The team aims to produce both fundamental scientific contributions and working experimental systems, with opportunities for publications, international collaborations, and interaction with industry.

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

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 researchers with strong backgrounds 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
• Embedded and low-power systems
• Signal-processing hardware and hardware/software co-design

We are particularly interested in candidates who have deep expertise in one or more relevant areas and are motivated to work across the boundaries between signal processing, hardware, and experimental systems.

For example, a candidate with a strong background in statistical inference but limited GNSS experience may be an excellent fit, as may be a GNSS researcher interested in fundamentally different receiver architectures, or an embedded/analog researcher interested in developing new signal-processing approaches.

Prior GNSS experience is therefore valuable but not a prerequisite. The ability to formulate and investigate research questions, develop rigorous models and algorithms, and validate ideas experimentally is more important than matching every item in the preferred-knowledge list.

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.

Website for additional job details: https://selection.uoc.edu/web/offersjob/offerdetails.aspx?offerID=F5E784539304A31216BAA961234975DCC7838164ADAAE523C23A5B28C6764F29

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