Postdoctoral Fellow in Biomedical Engineering with a focus on Neurotechnology

Lund University was founded in 1666 and is repeatedly ranked as one of the world's leading academic institutions. It is home to approximately 46,000 students and 8,500 employees in Lund, Helsingborg, and Malmö. We are united in our endeavor to understand, explain, and improve our world and human conditions.

Are you an expert in computational modeling, machine learning, or signal processing and have a strong interest in neuromuscular physiology? Are you interested in tackling challenging problems within neuromuscular physiology and developing scalable algorithms for large biomedical datasets? Are you aiming for a research career in academia and want to work in a collaborative, interdisciplinary environment?

Description of the workplace

The position is located within the Neuroengineering research environment at the Department of Biomedical Engineering, LTH, Lund University. The group conducts internationally recognized research on neuromuscular interfaces, high-density surface electromyography (HD‑sEMG), prosthesis control, and computational analysis of large-scale biosignals, in close collaboration with clinical and academic partners. A central focus for the group is the development of advanced computational methods to decompose HD‑sEMG into motor unit activity through optimization, inference, and Bayesian modeling. This work is closely linked to GPU‑accelerated high-performance computing through Lund University's national e‑infrastructure.

You will be part of an interdisciplinary research environment that values scientific independence, openness, and methodological innovation, with a strong emphasis on reproducible research, open source, and high-quality publications.

Specific subject description

The project concerns the development of computational methods to process and analyze HD‑sEMG recordings to extract detailed information about the neural control of movement. The long-term goal is to further develop techniques for neuromuscular diagnostics, prosthesis control, and rehabilitation.

The work will focus on large-scale inverse modeling and data-driven analysis of complex biosignals, implemented using high-performance computing infrastructure. The project is carried out in close collaboration with national and international partners and includes the development of open-source research software.

Tasks

The tasks as a postdoctoral fellow primarily consist of conducting research. Teaching may also be included in the tasks, however, at most one-fifth of the working time. Within the framework of the employment, there is an opportunity for three weeks of higher education pedagogical training. The purpose of the employment is to develop one's independence as a researcher as well as to create conditions for further meritation.

Your research work will consist of developing and implementing advanced computational methods for the analysis of high-density surface electromyography (HD‑sEMG) and related neuromuscular data. The work combines method development with the analysis of experimental data and close collaboration with national and international research partners.

The tasks include:

Conduct research in biomedical engineering and neuromuscular signal analysis Develop, implement, and evaluate large-scale data analysis methods using high-performance computing infrastructure Analyze experimental HD‑sEMG data, generate simulated data, and contribute to experimental design and validation Publish research results in international journals and present at scientific conferences Contribute to the supervision of master's students and doctoral students Actively participate in collaborative projects and contribute to applications for external research funding Perform administration related to the tasks above

Eligibility

Those who have obtained a doctoral degree, or a foreign degree assessed to correspond to a doctoral degree, within the subject area of the employment are eligible to be employed as a postdoctoral fellow. Proof that the eligibility requirement is met must have been submitted no later than the time when the employment decision is made. Primarily, the applicant in question will be someone who obtained their degree at most three years before the application deadline. If there are special reasons, the doctoral degree may have been obtained earlier.

Other requirements

Very good knowledge of English, both oral and written Experience in computational modeling, signal processing, machine learning, statistical inference, scientific programming, or related data-driven methods, including programming in Python or similar high-level languages Scientific competence and methodological skills relevant to computational biomedical engineering, including analysis of biomedical signals such as EMG, EEG, MEG, or related time-series data Ability to work independently and take responsibility for driving research projects forward Good collaborative skills and the ability to work in an interdisciplinary research environment Scientific publications in international peer-reviewed journals within a relevant field

For a full description, see: https://lu.varbi.com/what:job/jobID:946860/