Doctoral student in deep learning for mass spectrometry data Full-time

Doctoral Student In Deep Learning For Mass Spectrometry Data

Third-cycle subject: Biotechnology

The project aims to develop probabilistic deep learning models to improve the interpretation and matching of mass spectrometry data, a field in which Sweden holds a leading global position. To this end, the doctoral student will use and develop computational tools, and a good understanding of proteomics data will be important for interpreting results. The position is part of the DDLS (Data-Driven Life Science) Research School. The doctoral student will primarily work together with their supervisors, as well as with other doctoral students and postdocs in the supervisors' groups. Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create globally leading computational and data science capabilities in Sweden. The program is funded with a total of 3.3 billion SEK over 12 years from the Knut and Alice Wallenberg (KAW) Foundation.

In 2026 the DDLS Research School will be expanded with the recruitment of 25 academic and 7 industrial PhD students. During the course of the DDLS program more than 260 PhD students and 200 postdocs will be part of the Research School. The DDLS program has four strategic research areas: cell and molecular biology, evolution and biodiversity, precision medicine and diagnostics, epidemiology and biology of infection. For more information, please see https://www.scilifelab.se/data-driven/ddls-research-school/

The future of life science is data-driven. Will you be part of that change? Then join us in this unique program!

At KTH, we are announcing the position as DDLS PhD student in Data driven cell and molecular biology. Data driven cell and molecular biology covers research that fundamentally transforms our knowledge about how cells function by peering into their molecular components in time and space, from single molecules to native tissue environments.

Supervision: Lukas Käll is proposed to supervise the doctoral student. Decisions are made on admission

Admission Requirements

To be admitted to postgraduate education (Chapter 7, 39 § Swedish Higher Education Ordinance), the applicant must have basic eligibility in accordance with either of the following:

• passed a second cycle degree (for example a master's degree), or
• completed course requirements of at least 240 higher education credits, of which at least 60 second-cycle higher education credits, or
• acquired, in some other way within or outside the country, substantially equivalent knowledge

In addition to the above, there is also a mandatory requirement for English equivalent to English B/6.

Selection

In order to succeed as a doctoral student at KTH you need to be goal oriented and persevering in your work. During the selection process, candidates will be assessed upon their ability to:

• independently pursue his or her work
• collaborate with others,
• have a professional approach and
• analyze and work with complex issues.

The following qualities and experiences are considered meriting beyond the general selection criteria:

• Experience in machine learning or deep learning, preferably with transformer architectures
• Experience in probabilistic modelling or Bayesian statistics
• Programming skills in Python, preferably with PyTorch or similar frameworks
• Familiarity with mass spectrometry-based proteomics

After the qualification requirements, great emphasis will be placed on personal skills.