Darko Zibar

This PhD position at the Technical University of Denmark (DTU) offers an exciting opportunity to advance the field of electromagnetic wave interaction with lossy materials, such as human tissue, through the innovative use of machine learning in the design of antennas and metasurfaces. The project is a collaborative initiative between DTU Space and DTU Electro, with industrial benchmarking support from HHC Medical. The research will focus on the theoretical, numerical, and experimental aspects of machine learning-assisted design for efficient propagation and focusing of electromagnetic waves in various lossy media, including in vitro sensing of biological samples. As a PhD student, you will engage in a range of activities, including microwave measurements of dielectric properties, development of simulation models for electromagnetic wave interaction, machine learning-based design of antennas and metasurfaces, experimental validation of near-field interactions, and prototype fabrication and assessment. The project aims to connect electromagnetic performance with biological outcomes, leveraging benchmarking by HHC Medical using biological assays. The position offers a unique chance to develop expertise in antennas and metasurfaces for biomedical applications, machine learning techniques for electromagnetic device design, and advanced measurement techniques. You will benefit from close collaboration with leading research groups at DTU and interaction with industrial partners. The PhD program includes 30 ECTS of tailored coursework, covering both technical and soft skills, and provides a supportive international research environment. Applicants should have a strong background in electromagnetics and antennas, with solid knowledge of metasurfaces and preferably experience in machine learning. Experimental skills and familiarity with applications of machine learning in electromagnetics are advantageous. A two-year master's degree (120 ECTS) or equivalent is required. Proficiency in English is essential. The position is fully funded for 3 years, with salary and terms based on the Danish Confederation of Professional Associations' agreement. To apply, submit your complete application online by 19 December 2025, including a cover letter, CV, grade transcripts, and diplomas in English as a single PDF file. For more information about the research environment, visit the DTU Space and DTU Electro websites. DTU is committed to diversity and encourages applications from all qualified candidates, regardless of background.

The Technical University of Denmark (DTU) Electro invites applications for a fully funded PhD position in digital signal processing and machine-learning-enabled continuous-variable quantum key distribution (CV-QKD). This position is part of the Marie Skłodowska-Curie QuNEST – Quantum Enhanced Optical Communication Network Security doctoral training program and the Villum Investigator Program: Power-Efficient Optical Communication (POPCOM). The PhD project focuses on developing advanced machine-learning-based signal-processing frameworks, including constellation and pulse shaping, as well as equalization, to address impairments in practical CV-QKD systems such as bandwidth constraints, ADC resolution, and phase noise. Emphasis is placed on energy-efficient, low-complexity hardware solutions. The research will involve both algorithm design and experimental implementation, with opportunities for collaboration and research stays at Nokia Bell Labs (France) and the University of Warsaw (Poland). Key research areas include: Machine learning techniques for quantum communication system design Data-driven modeling and optical performance monitoring Experimental procedures for testing developed frameworks Building and evaluating transmission systems for quantum communication Investigating transmission performance in practical test-beds As a PhD candidate, you will participate in a tailored educational program (30 ECTS) covering both technical and soft skills, and you will be responsible for conducting research on machine-learning-based methods to mitigate system and channel impairments in quantum communication. The goal is to maximize secure key rates and transmission distances while minimizing energy consumption. Specific tasks include gradient-based learning for signal equalization and demodulation, model-free reinforcement learning for signal pre-distortion, extracting information from correlation matrices, maintaining a GitHub repository, and organizing joint experiments with collaborators. Eligibility and Requirements: Applicants must hold a two-year master's degree (120 ECTS) or equivalent in a relevant field (fiber optics, quantum optics, machine learning, communication engineering) and have some international experience. Candidates must not have resided or carried out their main activity in Denmark for more than 12 months in the 36 months prior to recruitment. Experience with machine learning in communication systems and hands-on experimental work is advantageous. Proficiency in English is required. You may apply before obtaining your master's degree but cannot start before receiving it. Funding and Employment: The position is fully funded for 3 years, with salary and terms based on the collective agreement with the Danish Confederation of Professional Associations. The start date is flexible, preferably before 1 March 2026. Application Process: Submit your application online by 1 January 2026. Applications must include a cover letter, CV, grade transcripts, diplomas, and a description of the grading scale, all in English and combined into a single PDF. Late applications will not be considered. DTU is a leading technical university with a strong international environment, offering excellent research, education, and innovation opportunities. The university values diversity and inclusion and encourages applications from all qualified candidates. For further information, contact Professor Darko Zibar at [email protected]. More details about the department and project can be found at DTU Electro and QuNEST .