BEMÆRK: Ansøgningsfristen er overskredet
We are looking for PhD candidates to join the TACsy (Training Alliance for Computational Systems Chemistry) Marie-Skłodowska-Curie Joint Doctoral Network. This network consists of fifteen (15) highly interlinked PhD projects, of which this particular call is for project DC 13: Computational Lipidomics and Mass Spectrometry – Learning Mechanistical Models. The application deadline is 23 October 2023. The anticipated start date is 1 January 2024, but flexibility in both directions is possible.
Several approaches developed within TACsy are based on a graph transformation language which is used to expand chemical spaces, similarly to how production rules in formal languages are used to derive words of a formal language. Both metabolic networks and the fragmentation process within a mass spectrometer can be modelled as a rule-based system. The latter has the advantage that the combinatorics are far less dramatic, since fragmentation only splits (never merges) chemical compounds. Modelling fragmentation patterns as graph transformation rules, however, is a challenge and requires automation.
Within this project the PhD student will develop novel machine learning algorithms for automated rule inference in the mass spectrometer setting. For the subsequent spectrum prediction these results will be integrated with approaches that stochastically simulate the fragmentation process in a mass spectrometer. The PhD student will have a 12 month research stay with our industry partner Thermo Fisher Scientific, a world leader in instrumentation and mass spectrometry.
In addition to developing novel algorithms for rule inference and mass spectrum prediction, the PhD student will have the chance to apply them in a flagship project of TACsy (in collaboration with our scientific partner EMBL): After modelling lipid metabolic networks as graph transformations, the computational framework developed will support our partner’s functional studies of how the lipid metabolic networks of mouse embryonic stem cells are remodelled during cell fate acquisition.
We are seeking an excellent and highly motivated individual with either an MSc degree in computer science, an MSc degree in Applied Mathematics, or an MSc degree in chemistry/physics with a strong focus on computational methods. The ideal candidate has familiarity with one or more of the following areas: machine learning, data mining, knowledge discovery, stochastic simulations, algorithmics, graph theory, graph transformation, algorithm engineering. Proven competences in programming and ease with formal thinking are a necessity.
See details about the network as a whole, its scientific content, and the application and selection process here.
Please note that there are two limitations to apply for this programme, as specified in the MSCA regulations. Applicants must:
- Not be in possession of a PhD degree at the date of recruitment.
- Meet the Marie-Curie mobility rule (see pages 81-82 here). In TACsy, this means the applicants are not eligible for a specific position if it is true for both host institutions of the position that the applicant for more than 12 of the last 36 months has resided or performed a main activity (work, studies, etc.) in the country of that host institution. The host institutions relevant to this rule are for the present call located in (DC 1: Denmark and Germany), (DC 3: Austria and Germany), (DC 6: Austria and Germany), (DC 12: Austria and Denmark), (DC 13: Austria and Denmark).
These positions are subject to the entry into force of grant agreement no 101072930 by and between the European Research Executive Agency and University of Southern Denmark and thereby funded by the European Union’s Horizon Europe Research and Innovation Programme.
Please see the full call, including how to apply on www.sdu.dk.
INFORMATIONER OM STILLINGEN:
- Arbejdspladsen ligger i:
Odense Kommune
-Virksomheden tilbyder:
-Arbejdsgiver:
Syddansk Universitet, Campusvej, 5230 Odense M
-Ansøgning:
Ansøgningsfrist: 23-10-2023; - ansøgningsfristen er overskredet
Se mere her: https://job.jobnet.dk/CV/FindWork/Details/5914167
