The Chair for Bioinformatics, Institute of Computer Science at the Friedrich-Schiller-University Jena offers the following position:
PhD Student for EU HUMAN Doctoral Network (Wissenschaftliche Mitarbeiterin)
The doctoral network HUMAN (Harmonising and Unifying Blood Metabolomic Analysis Networks) is a multidisciplinary consortium that focuses on topics such as cross-laboratory comparisons, integration, co-evaluation, and proofing of liquid chromatography mass spectrometry for the analysis of human blood. It offers twelve PhD positions.
We are looking for talented PhD student from the research field of Bioinformatics or Computer Science (machine learning, algorithm development) who is passionate about research and have a profound knowledge in a) computational methods, development and evaluation and b) machine learning, development and evaluation. Basic knowledge in biology, biochemistry, chemistry is desirable; knowledge about mass spectrometry would be helpful as well but not required. The project will involve international and intersectorial secondments/visits to other project partners in the network for the doctoral candidates to learn new skills and foster collaborations.
Salary is according to EU regulations: The gross salary (not including employer’s social contributions) is 3264 € per month, or 3811 € per month including a family allowance (if applicable). Positions are temporary appointments. Handicapped applicants will be given preference in case of equal qualifications.
Please apply for the position via https://human-dn.eu/doctoral-candidates/.
Project description: In silico methods for searching small molecule MS/MS data in structure databases are becoming an everyday tool in untargeted metabolomics research. Yet, we presently have no idea about how experimental parameters influence metabolite annotation power and reproducibility. Here, we will approach this problem, assessing metrics that researchers are usually interested in: How many signals in the data can we annotate with a metabolite structure; and how many of these annotations are correct? We will study this on different levels of annotation, such as compound class annotations. Using experimental standards will allow us to establish a ground truth to compare against. We will evaluate variations between labs and measurements if we vary experimental or instrument parameters.