PhD studentship in Bioinformatics / Data Science (with application to immunology)

(with application to immunology)

UIC, Barcelona, Spain

Objective

Applications are invited for the position of a PhD student, working under the supervision of Bernhard Knapp in the Department of Basic Sciences, Faculty of Medicine and Health Sciences, UIC University, Barcelona, Spain.

The work group of Bernhard Knapp (https://www.uic.es/bioinformatics) focuses on computational simulations of the (human) immune system. We provide novel insight in fundamental processes of the immune system by means of quantitative analysis and predictive models. More specifically the work focuses on how T-cell receptors (TCRs) recognise Major Histocompatibility Complex (MHC) bound peptides in different health and disease conditions as for example allergies, cancer, autoimmune diseases, or infections. Apart from TCR/peptide/MHC interactions also antibody/antigen interactions are a key research interest of the group.

We apply a wide range of computational methods for (biomedical) challenges. Examples of work include: by far the largest dataset of T-cell receptor recognition processes (Knapp et al., 2014), comparison of the molecular design (Dunbar et al., 2014) and instability (Knapp et al., 2017) of antibodies with T-cell receptors, obtaining novel insights in peptide/MHC detachment processes (Knapp et al., 2016), giving accurate and reproducible predictions of peptide/MHC binding affinities (Wan et al., 2015), optimising binding affinities using Genetic Algorithms (Knapp et al., 2011), and investigating protein/protein interactions (Esmaielbeiki et al., 2016), and development of tools (Knapp et al., 2018). Highly parallel calculations are carried out on different supercomputers.

Requirements

Must criteria:

• BSc and MSc in a quantitative field (e.g. computer science, statistics, math, (bio)physics, bioinformatics).
• The average of all marks of the bachelor and master should be better than 0.75 (on a normalised scale of 0 (worst) to 1 (best)). Please separately state your average mark for your bachelor and master in your CV (and show supporting documents as an attachment). Applications not containing this information for the BSc or the MSc cannot be considered. Unfortunately, also applicants without a master degree (or who already hold a PhD) cannot be considered
• The MSc must have been obtained after the 1 November 2016
• Broad experience and fluency in several programming languages e.g. Python
• Strong statistics knowledge (univariate, multivariate)
• Strong analytical skills, creativity, curiosity, enthusiasm, and ability to work in a team
• Excellent command of the English language

Ideal criteria:

• Basic knowledge of molecular biology, genetics, immunology is an advantage but not required
• Previous experience with Monte Carlo simulations is an advantage
• Knowlegde of data science techniques: Regression (e.g. ANN, Lasso, Ridge) , classification (e.g. SVM, KNN), clustering (e.g. k-means, hierarchical), heuristic optimisation (e.g. genetic algorithms), parallel computing (e.g. MPI, pySpark), SQL, noSQL is an advantage
• Knowledge of common bioinformatics techniques as Immunoinformatics, B- and T-cell epitope predictions, molecular dynamics simulations, free energy predictions, protein/ligand and protein/protein docking, virtual screening, protein structure prediction, sequence alignments, tree building is an advantage
• Knowledge of LINUX operating systems and command line operations is an advantage
• Experience with the use of high performance computing clusters is an advantage
• Previous teaching experience in English and/or Spanish is an advantage

Offer

• The PhD position is funded by the EXCELENCIA program of the Spanish ministry of economics, industry and competitiveness
• The successful candidate will start his/her work in an exciting project in the field of computational simulations of the immune system
• Access to high performance computers is available
• Funding to go to conferences is available
• The successful candidate can start immediately

Application documents (as one pdf file)

• CV
• Publication list (if existing) including journal impact factors
• Letter of motivation (short, max 1 page) answering to each of the “must-criteria” as stated above. Especially state the marks average of your BSc and MSc.
• Contact details of 2-3 references

 Before you apply please keep in mind that common reasons for not being invited for an interview are: (1) not fulfilling one or multiple of the "must criteria", (2) not following the application guidelines, (3) generic copy & paste applications, (4) low level of English, and (5) or missing of the deadline.

Applications will be reviewed continuously on a 'first come first served' basis and as soon as an appropriate candidate is found the position will be closed. However, the call will close latest by the 9th of September 2018.

Dunbar,J., Knapp,B., Fuchs,A., Shi,J., and Deane,C.M. (2014). Examining Variable Domain Orientations in Antigen Receptors Gives Insight into TCR-Like Antibody Design. PLoS Comput Biol 10, e1003852.

Esmaielbeiki,R., Krawczyk,K., Knapp,B., Nebel,J., and Deane,C.M. (2016). Progress and Challenges in Predicting Protein-Protein Interfaces. Brief Bioinform 17, 117-31.

Knapp,B., Alcala,M., Zhang,H., West,C., van der Merwe,P.A., and Deane,C.M. (2018). pyHVis3D: Visualising Molecular Simulation deduced H-bond networks in 3D: Application to T-cell receptor interactions. Bioinformatics.

Knapp,B., Demharter,S., Deane,C.M., and Minary,P. (2016). Exploring peptide/MHC detachment processes using Hierarchical Natural Move Monte Carlo. Bioinformatics 32, 181-186.

Knapp,B., Dunbar,J., Alcala,M., and Deane,C.M. (2017). Variable Regions of Antibodies and T-cell Receptors may not be Sufficient in Molecular Simulations Investigating Binding. J. Chem. Theory Comput. accepted.

Knapp,B., Dunbar,J., and Deane,C.M. (2014). Large Scale Characterization of the LC13 TCR and HLA-B8 Structural Landscape in Reaction to 172 Altered Peptide Ligands: A Molecular Dynamics Simulation Study. PLoS Comput Biol 10, e1003748.

Knapp,B., Giczi,V., Ribarics,R., and Schreiner,W. (2011). PeptX: Using Genetic Algorithms to optimize peptides for MHC binding. BMC. Bioinformatics. 12, 241.

Wan,S., Knapp,B., Wright,D., Deane,C., and Coveney,P.V. (2015). Rapid, Precise and Reproducible Prediction of Peptide-MHC Binding Affinities from Molecular Dynamics that Correlate Well with Experiment. J. Chem. Theory Comput. 11, 3346-3356.