A variety of PhD, Postdoc and Clinical Fellow positions are currently open (as of 29/9/23), or shall be opening shortly. Links to positions are provided under specific tasks below. Alternatively, please send your c.v. to firstname.lastname@example.org with a one paragraph letter of motivation that indicates the work-package and task(s) below that match your interests and experience. Formal applications at the corresponding institutions may subsequently be solicited.
WP1. Clinical recruitment
Partners: ASUFC, UKHD, UMCG-Derks, TUB-Graessner, TUB-Haack, UCL-Rahman, UNIAMO.
1.1 Coordination of patient recruitment and data stewardship
1.3 Recruitment via established IMD patient registries
1.4 Recruitment from Solve-RD
WP2. Metabolic network-based classification
Partners: NUIG-Thiele, NUIG-Fleming, UMCG-Bakker, ULEI-Hankemeier, TUM-Prokisch.
2.2 Proteomic analysis of patient fibroblast samples
2.3 Prediction of metabolic biomarkers using whole-body metabolic modelling
2.4 Prediction of causal genes using whole-body metabolic modelling
2.5 Extension of whole-body metabolic models with kinetic constraints
2.6 Clinical deployment of software for metabolic network-based IMD diagnosis
WP3. Enzyme structure-guided classification
Partners: UNEW-Yue, UOX-Marsden
3.1 Prediction of the three-dimensional structure of enzyme variants
3.2 Experimental characterisation of dysfunctional enzyme variants
3.3 Development of software for automated enzyme structure-guided classification
3.4 Identification of druggable enzyme variants
WP4. Classification of genomic variants of unknown significance
Partners: TUB-Haack, UOX-Marsden, NUIG-Thiele
4.1 Selection of patients with genomic variants of known and unknown significance
4.2 Prediction of causative gene variants by personalised whole-body metabolic modelling
4.3 Prediction of dysfunctional amino acid variants by modelling of enzyme structure
4.4 Development of clinically accessible software for interpretation of genomic variants
WP5. Reconstruction of human metabolic networks
Partners: NUIG-Fleming, SIB-Bridge, SIB-Pagni, UOS-Holthuis, NUIG-Thiele.
5.1 ReconXKG: an open workspace for human metabolic network reconstruction
5.4 Development of enhanced sex-specific, whole-body metabolic models
WP6. Personalised disease modelling
Partners: ASUFC-Scarpa, ULEI-Aerts, ULEI-Hankemeier, NUIG-Fleming.
6.1 Personalised management of Gaucher disease patients
6.2 Optimisation of personalised therapies for Gaucher disease
6.3 Metabolomic analyses of Gaucher disease
6.4 Computational modelling of cell-type specific metabolism in Gaucher disease
6.5 Fluxomic analyses of in vitro models of Gaucher disease
6.6 Prioritised personalised disease modelling of additional IMDs
WP7. Development of Software Medical Devices
Partners: DKIT-McCaffery, TUB-Haack, UOX-Marsden, NUIG-Fleming, NUIG-Thiele).
7.1 A novel software process assessment model for AI-enabled software medical devices
7.2 Translation of academic software into software medical devices
7.3 Clinically accessible personalised modelling software for IMDs
Partners: UNIAMO, ASUFC, UKHD-Koelker, TUB-Haack, NUIG-Fleming, ULEI-Hankemeier
8.1 Assessment of stakeholder perspectives on the exploitation of novel diagnostic technologies
8.2 A plan for a European foundation for personalised diagnosis and management of IMDs
WP9. Dissemination, training and outreach
Partners: NUIG-Fleming and others.
9.1 Establish the overall communication and dissemination framework
9.2 Coordinate and implement communication and dissemination frameworks
9.3 Establish a continuous learning environment
9.4 Establish the Recon4IMD IP and data management frameworks
Specific positions currently being advertised
Senior Researcher for Reconstruction and Computational Modelling for Inherited Metabolic Diseases (Recon4IMD) project.
This is a senior position in the Regulated Software Research Centre (RSRC) within the School of Informatics and Creative Arts in DkIT. The successful candidate will join the Reconstruction and Computational Modelling for Inherited Metabolic Diseases (Recon4IMD) project.
Salary Range: Postdoc PD1.1 to PD1.4 - €42,783 to €46,569 depending on experience
Application Deadline: 12 noon (Irish Time) Friday 1st December
Postdoc in Reconstruction and Computational Modelling for Inherited Metabolic Diseases School of Medicine Ref. No. University of Galway XXX-XX (from hr) Applications are invited from suitably qualified candidates for a full-time, fixed term position as a postdoctoral scholar within the School of Medicine at the University of Galway. This position is funded by the European Union and is available immediately until contract end date of 31/05/27. Reconstruction and Computational Modelling for Inherited Metabolic Diseases is a Horizon Europe funded collaborative project, between researchers, clinician and patients, which aims to accelerate the diagnosis, and enable personalised management, of inherited metabolic diseases. For further information, please consult the project website: www.recon4imd.org Job Description: The successful candidate will develop and validate novel methods for prediction of causal genes using whole-body metabolic modelling of inherited metabolic disease patients. Personalised whole-body metabolic models will be used to predict the metabolic gene defect(s) required in silico to recapitulate the metabolite concentration changes measured by conventional metabolic screening and additional untargeted metabolomic analysis of blood and urine samples from inherited metabolic disease patients. To achieve this, the candidate will develop a novel algorithm, based on a sequence of convex optimisation problems, which predicts a minimal number of gene perturbations required to closely approximate the abnormal metabolite concentration changes (relative to controls) measured in clinical biofluid samples. This algorithm will be trained and refined using data on a set of patients with genetic perturbations of known inherited metabolic disease significance. Specifically, physiological parameters will be used to personalise the whole-body metabolic models and the difference between predicted and measured metabolite concentration changes, obtained from targeted metabolic analysis and additional untargeted metabolomic analysis of blood and urine samples, will be used as an objective within the algorithm to predict causal gene perturbations. The predicted causative metabolic genes will be compared with the genes associated known inherited metabolic disease significance to iteratively refine the algorithmic approach. Given measured metabolome changes, prediction of causal genes will be applied to accelerate the diagnosis of inherited metabolic disease patients, aid classification of genetic perturbations, and predict novel metabolite biomarkers. The successful candidate will also be engaged in supervision of a PhD student responsible for the following task: Computational approaches for the identification of candidate causal gene defects and associated biomarkers for patients at risk of an IMD will be made accessible to IMD clinicians by developing a web interface that communicates with a dedicated remote server. The clinician will be able to select a patient within the U-IMD registry and, under the hood, a U-IMD API will pipe the physiological parameters as well as the results of any genetic or metabolic diagnostic tests on that patient, to the remote server where it will be integrated with the results of metabolomic analysis. Remote personalised computational modelling will then return to the clinician a rank-ordered prediction of candidate causal genes whose perturbation recapitulates the input data. Leveraging the Virtual Metabolic Human infrastructure, relevant metabolic pathways and explanations will be also provided to put the predictions into a disease-specific metabolic network context. The web interface will be developed together with clinical partners to ensure it is relevant and easy to use in daily clinical practice and with regulated software developers, to ensure that software medical device requirements are met. Metabolic network-based classification will be made clinically accessible to enable the prediction of causal gene defects, thereby accelerating IMD diagnosis. Duties: • Conducting research in the designated area and contributing to the development of new ideas and techniques. • Collaborating with the research team to design and perform computational experiments, collect and analyse data, and prepare manuscripts for publication. • Presenting research findings at conferences and workshops. • Participating in grant proposal writing and fundraising activities. • Mentoring graduate and undergraduate students. Qualifications/Skills required: LIST essential and desirable criteria in bullet points from the Post Initialising Form - these will serve as the shortlisting criteria in the recruitment and selection process Essential Requirements: • A Ph.D. degree in systems biology, computational biology, numerical optimisation or related disciplines. • A strong publication record, relative to academic maturity, in peer-reviewed journals. • Experience with metabolic modelling, constraint-based modelling, and formulation of optimisation problems. • Excellent written and verbal communication skills in English. • Strong organizational and time-management skills. • Ability to work independently and as part of a team. Desirable Requirements: • Experience with Julia or Matlab • Experience with COBRA software • Experience with high performance computing • Experience with proposal writing Employment permit restrictions apply for this category of post Salary: 41,209-53,091 per annum depending on experience (public sector pay policy rules pertaining to new entrants will apply). Start date: Position is available immediately. Continuing Professional Development/Training: The University of Galway provides continuing professional development supports for all researchers seeking to build their own career pathways either within or beyond academia. Researchers are encouraged to engage with our Researcher Development Centre (RDC) upon commencing employment – see www.universityofgalway.ie/rdc for further information.' Further information on research and working at University of Galway is available on Research at University of Galway For information on moving to Ireland please see www.euraxess.ie Informal enquiries concerning the post may be made to Associate Professor Ronan Fleming via email@example.com To Apply: Applications to include a letter of motivation, CV, and the contact details of three referees should be sent, via e-mail (in word or PDF only) to Associate Professor Ronan Fleming: firstname.lastname@example.org Please put reference number University of Galway 105-23 in subject line of e-mail application. Closing date for receipt of applications is 5.00 pm 3/5/23 We reserve the right to re-advertise or extend the closing date for this post. The University of Galway is an equal opportunities employer. All positions are recruited in line with Open, Transparent, Merit (OTM) and Competency based recruitment