Vacant job

Lund University was founded in 1666 and is consistently ranked among the world's leading universities. We have approximately 46,000 students and 8,500 staff members in Lund, Helsingborg, and Malmö. We are united in our strive to understand, explain, and improve our world and the conditions of people.

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

Workplace Description

The position is located within the research environment of Neuroengineering at the Department of Biomedical Engineering, LTH, Lund University. The group conducts internationally recognized research on neuromuscular interfaces, high-density surface electromyography (HD-sEMG), prosthetic 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 involves 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, prosthetic control, and rehabilitation.

The work will focus on large-scale inverse modeling and database-based 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.

Job Responsibilities

The primary responsibility of the postdoctoral position is to conduct research. Teaching may also be included in the duties, but no more than one-fifth of the working time. Within the framework of the employment, there is an opportunity for three weeks of university pedagogical education. The purpose of the employment is to develop your independence as a researcher and create conditions for further career advancement.

Your research 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 responsibilities include:

• Conducting research in biomedical engineering and neuromuscular signal analysis
• Developing, implementing, and evaluating large-scale data analysis methods using high-performance computing infrastructure
• Analyzing experimental HD-sEMG data, generating simulated data, and contributing to experimental design and validation
• Publishing research results in international journals and presenting at scientific conferences
• Contributing to the supervision of master's students and PhD students
• Actively participating in collaborative projects and contributing to applications for external research funding
• Performing administration related to the above duties

Qualifications

To be eligible for employment as a postdoctoral researcher, you must have completed a PhD degree, or a foreign degree assessed as equivalent to a PhD, within the subject area of the employment. Proof that the eligibility requirement is met must be received no later than at the time the employment decision is made. Preference will be given to applicants who have completed their degree no more than three years before the last application date. If there are special reasons, the PhD degree may have been completed earlier.

Other Requirements

• Very good knowledge of English, both spoken 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 the 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 collaboration skills and ability to work in an interdisciplinary research environment
• Scientific publications in internationally peer-reviewed journals in a relevant field

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