PhD Student in Computational Immunology - Nextimmune DTU - LCSB
29, rue Henri Koch, Esch-sur-Alzette L-4354, Luxembourg
NEXTIMMUNE, standing for “Next Generation
ImmunoScience: Advanced Concepts for Deciphering Acute and Chronic
Inflammation”, is a competitive PhD training program, supported by the doctoral research funding scheme PRIDE of the Luxembourg
National Research Fund FNR. It aims to bridge classical immunology and big-data
analysis science in a structured doctoral training environment. NEXTIMMUNE is opening.
with up to 4 years fixed-term contract, full-time. Start date flexible from now on until July 2018. Supervisor: Prof. Jorge Goncalves, Systems Control Group at the Luxembourg Centre for Systems Biomedicine (LCSB) of the University of Luxembourg.
The research-intensive program aims to respond to the unmet need of training the next generation of competent immunologists by tackling next generation immunology challenges from wet lab procedures to big data analyses. We offer an interdisciplinary environment that covers analysis of “omics” and clinical data, as well as basic and translational biomedical knowledge combined with its practical application to diagnosis and ultimately therapy. The program includes transferable skills training, support in career development, scientific lectures by international speakers and annual PhD retreats.
A main research interest within the Systems Control Group at the University of Luxembourg is network inference, which means to address the inverse problem consisting in inferring from time-series data the topology and properties of the underlying network which generated the data. Within the scope of the NextImmune Doctoral Training Unit, following the dynamic measurements in different patients, Jorge Goncalves’s group will use established computational tools adapted from the field of control engineering to identify potential regulatory causality between genes in Th2 cells, the major adaptive immune cells of allergic inflammation. This will provide a directed network of cause-effect relationship between measurements (genes). Strong mathematical background is a requirement. Hence, the student must hold a mathematics, engineering or physics degree. If not already covered in their background, students must also learn advanced mathematic courses from the mathematics department including analysis, functional analysis and linear algebra. Biological knowledge is not essential.
• Hold (or being about to obtain) a Master degree in Mathematics, Theoretical Physics, Control Systems Engineering, Theoretical Machine Learning or related fields.
• Strong mathematical background is a requirement.
• We will only consider students that graduate in their top 20% undergraduate and Master’s class rank (equivalent to a UK first class degree).
• Excellent working knowledge of
Applications including a motivation letter describing past research experience and future interests, at least two confidential references letters, a full curriculum vitae and a copy of the relevant diplomas showing marks should be sent via the apply button. Only complete applications will be considered.