Microelectronics and Sensors

Analogue application specific integrated circuit (ASIC) design when ultra-low-power and high integration are essential, with particular focus on sensor interfacing and signal conditioning. Analogue signal processing and instrumentation for electrophysiological signal recordings and (bio)-sensor and biosample analysis. Battery-powered and battery-less system development.
Electrochemical (amperometric and potentiometric sensors for pH, Na+, K+, glucose lactate), physical (temperature and strain sensors), impedimetric (bioimpedance and electrochemical impedance spectroscopy), optical (photodetectors and photoplethysmography) and electrophysiological (ECG, EMG, EEG) sensors. Design and fabrication passive mixers and pumps and fluidic channels with integrated sensing capabilities for point-of-care analysis of biosamples. Fabrication methods for flexible/stretchable electronic devices, centered mainly around interconnect and sensor technology, elastomeric and plastic materials, conductive polymers and composites and liquid metals. Electrical (IV characteristics, Van der Pauw conductivity measurements, impedance spectroscopy) and material characterization methods (SEM, XPS, AFM, EDS, Raman spectroscopy, FT-IR).

Software Development

Software Design with focus on human-machine interaction and user experience. Software architecture for extendable, modular and maintainable solutions with focus on performance, low fault tolerance, scalability and reliability, using software architecture patterns (microservices architecture, serverless architecture, event-driven architecture). Software security assessing performing architecture risk analysis, threat modeling, security control design analysis and managed penetration testing.
Developer Operations for planning product’s iteration development (using the Agile Software Development Life Cycle), testing and deployment to the production environments, deliver product updates and monitor log software performance.

Artificial Intelligence and Deep Learning

Applications of AI in biomedicine and diagnostics, Deep Learning based Automatic Detection, Application of Machine Learning in medical records, Support clinical decision making applications.
Deep Learning: Applications in medical research, Applications in classification of histology image datasets, Improve safety and security, Accelerated Deep Learning models supported by GPUs, Training using heterogeneous hardware.
Big Data:  Data exploration and model development, Decision optimization via investigation, solution comparison and scenarios evaluation, Predictive models and analysis applied to business and medicine.
Distributed Learning: Applications on Big Data analytics, Hybrid models employing data parallelism, Healthcare applications, Image classification and computer vision.

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