We are actively looking for highly motivated researchers to join our international, interdisciplinary, and dynamic team.
Openings may be available for
- Master students
- PhD students
- Postdoctoral fellows
- Software engineers
Applications can be sent directly to Prof. Ines Thiele: ines.thiele(at)nuigalway.ie and should contain a detailed CV and a cover letter detailing the researcher's interest in our research and for joining our research group.
Funded PhD position:
"Investigating the role of gut microbiome in
Alzheimer’s Disease using computational modelling and big data analysis"
Ref. No. NUIG-AD1
Applications are invited from suitably qualified candidates for a full-time fixed term position as a PhD student in the recently established Molecular Physiology Research group, led by Prof. Ines Thiele, at the National University of Ireland, Galway.
This position is funded by National Institute of Ageing (NIA, USA) and is available from May 1st, 2020 for three years.
Dementia is the leading cause of dependence and disability in the elderly population worldwide with Alzheimer’s disease (AD) being by far the most common form. The absence of effective therapies causes major socioeconomic burden. Increasing evidence supports that AD is a metabolic disease and a wide range of metabolic perturbations occur early in the disease process. While genetic risk clearly plays an important role in AD, the gut microbiome, environment and life-style might contribute to disease pathogenesis. Importantly, gut microbes complement human metabolism. For example, the “gut-brain metabolic axis” facilitates bi-directional chemical communication between the central and enteric nervous system through neuroactive metabolites, such as hormones and neurotransmitters. Changes in gut microbiome composition have been associated with a wide array of conditions, including neurological and neurodevelopmental disorders, such as depression, schizophrenia, and Parkinson’s disease. Many metabolites have recently been shown to be regulated in part by gut microbiome activity. This NIA-funded project brings together the power of metabolomics, (meta)genomics, and large clinical studies to gain further insights in the role of gut microbiome in AD mechanisms.
This PhD position is part of this international, interdisciplinary project and aims at using advanced computational and statistical analysis approaches to identify metabolic pathways involved in AD and cognitive decline. Therefore, metabolomic and metagenomic data will be computationally and statistically analysed in the context of large-scale computational models of host-microbiome co-metabolism.
Hertel et al., Integrated Analyses of Microbiome and Longitudinal Metabolome Data Reveal Microbial-Host Interactions on Sulfur Metabolism in Parkinson’s Disease, Cell Reports, 2019, link.
The successful candidate will analyse metagenomic and metabolomic data in the context of microbial and human metabolic reconstructions to investigate in silico host-microbiome co-metabolism and to predict personalised metabolic pathways perturbed in Alzheimer’s disease. The predictions will be carried out in close collaboration with our international collaborators. The project involves the developments of constraint-based modelling tools for host-microbiome metabolic modelling, integration of large-scale omics data with the host-microbiome models, application of advanced statistics, mining literature mining for relevant medical information.
Acquire profound understanding of key technologies and knowledge in computational systems biology, statistical analysis, (medical) biochemistry, and bioinformatics.
Perform the research as defined by the PI and the funding source.
Contribute to writing of scientific publications, progress reports, and grant applications.
Present at national and international conferences.
Acquire working skills in time and project management
Assist in the supervision of student projects and in teaching.
Participate in outreach activities, seminars, and workshops for the development of scientific and transferable skills.
Actively collaborate in an interdisciplinary, international team.
Engage actively with international collaborators.
Master degree (or equivalent) in Statistics, Bioinformatics, Computer Science, Biochemistry, Microbiology, or related fields.
Strong interest in metabolism, biochemistry, and computational modelling.
Demonstrable skills / experience in programming and/or statistical data analysis
Excellent communication skills are required.
Excellent working knowledge of English is required.
High motivation and interest in interdisciplinary research are required.
Demonstrable experience in the constraint-based reconstruction and analysis approach is highly desirable.
Experience in metagenomic and metabolomic data analysis is highly desirable.
Experience in statistics and machine learning is desirable.
Stipend: €18,000 per annum
Start date: Position is available from 01.05.2020
Continuing Professional Development/Training:
Researchers at NUI Galway are encouraged to avail of a range of training and development opportunities designed to support their personal career development plans.
Further information on research and working at NUI Galway is available on Research at NUI Galway
For information on moving to Ireland please see www.euraxess.ie
Further information about Molecular Systems Physiology Group is available at http://thielelab.eu.
Applications to include a covering letter, CV, a motivation letter, and the contact details of three referees should be sent, via e-mail (in word or PDF only) to Prof. Ines Thiele: e-mail
Please put reference number NUIG-AD1 in subject line of e-mail application. Note only applications with the reference number in the subject will be considered.
Closing date for receipt of applications is 5.00 pm 25.03.2020.
National University of Ireland, Galway is an equal opportunities employer.
All positions are recruited in line with Open, Transparent, Merit (OTM) and Competency based recruitment
Fully funded PhD Project:
"Conquering combinatorial explosion in biochemical network modelling"
Background: Comprehensive reconstructions of human metabolism have been formalised mathematically in constraint-based genome-scale models, in which reactions are connected to enzymes and their encoding genes, and in which the effect of genetic mutations on metabolic function can be simulated in silico. Genome-scale models can be made cell-type-specific and incorporate metabolic adaptations to age, gender, diet, lifestyle, or disease, by integrating transcriptome, proteome, and metabolome data. However, much of this progress is limited to small molecules, such as phosphorylated sugars, amino acids, and nucleotides, which are accurately mapped in computational models and relatively straightforward to measure. The metabolism of reaction networks involving polymers, which play a central role in many diseases, imposes fundamentally different challenges. Classically, enzymes are thought to have a specific affinity for one well-defined substrate. In contrast, enzymes acting on larger molecules catalyse the conversion of a functional group in the substrate with a lesser specificity for the remaining part of the molecule. This promiscuity results in a combinatorial explosion of different molecular species and biochemical reactions between these species, for example, in lipid metabolism. New mathematical and computational modelling techniques are required to address this apparent combinatorial explosion.
Objectives: The problem of combinatorial explosion of possible lipid molecular species in current computational models will be addressed by dividing the existing reactions for synthesis of complex lipids into a set of modular synthesis and degradation reactions, based on the concept of conserved moieties, resulting in a stoichiometric matrix that admits integral flows (reaction rates that are integers). Each integral flow shall represent the synthesis and degradation of a different combination of lipid molecular species. Disease relevant human lipidomics data will be mapped onto the metabolic network and analysed computationally in disease-specific scenarios.
Context: The candidate would join a 3 year PhD position within the recently awarded EU Initial Training Network, entitled “Polymers in the Liver: Metabolism and Regulation” (PoLiMeR). This network the first research training initiative in Systems Medicine. A mandatory requirement for any PhD student to avail of this funding instrument is that the primary location they carry out their PhD project would be a different country from the one they have resided in during the preceding years. The PhD position is accompanied by a full stipend, including a living allowance, mobility allowance and perhaps a family allowance if this is applicable. Two research secondments within the training network are envisaged. A Double Doctorate between Leiden University and National University of Ireland, Galway, is envisaged, with the majority of the time spent in Ireland.
This training network closes two fundamental gaps in European training. It educates young researchers in computational and experimental approaches to study the metabolism of biopolymers and other large metabolites, which are critical to making the next step in Systems Medicine, and (ii) offer them solid interdisciplinary training in the three ‘pillars of Systems Medicine’ - experimental, computational and clinical research. PoLiMeR’s training activities span innovative research in analytical biochemistry, organ-on-chip technology, computational modelling, data infrastructure and biomedical research – equipping our Early Stage Researchers (ESRs) with a holistic view from bench-to-bedside and a creative mind-set with special attention to future clinical/industrial applications of newly developed technology.
Mobility rule by the EU Successful candidates must fulfil the mobility criteria defined by the European Commission: At the time of recruitment by the host organisation, researchers must not have resided or carried out their main activity (work, studies, etc.) in the country of their host organisation for more than 12 months in the 3 years immediately prior to the reference date.
Expected Results: Replacement of stoichiometrically inconsistent lumped reactions in existing genome-scale models with a stoichiometrically consistent, modular representation of combinatorial lipid synthesis and degradation via integral flows. The initial focus shall be on lipid pathways of relevance to monogenic mitochondrial diseases. Adaption of existing constraint-based modelling software will be required to interrogate new models for combinatorial synthesis of lipid species.
Candidate: The ideal candidate would be an an interdisciplinary minded student with a keen interest in applied mathematics, computational modelling and biochemistry, with an interest in (a) learning about existing methods for mathematically modelling of genome-scale biochemical networks with constraint-based modelling methods and (b) looking to make an original applied mathematics contribution to the fundamental problem of how to model lipid metabolism without a combinatorial explosion in the number of distinct species. With respect to the latter, an interest in extending one's knowledge at the intersection of graph theory, hypergraph theory, (tractable) discrete optimisation and continuous optimisation is important.
Supervisor: Assist. Prof. Ronan Fleming, Head of the Systems Biochemistry group, Leiden University and Adjunct Lecturer at the National University of Ireland, Galway. The core expertise of the Systems Biochemistry Group is in fundamental research on computational modelling systems of biochemical reactions in general, and the application of this expertise to human disease. Dr. Fleming is the lead developer of the COBRA toolbox, including extensive production of tutorial training materials for the community: He is also the coordinator of the H2020 project “Systems Medicine of Mitochondrial Parkinson’s Disease” (2015-2019).
How to apply: Please send your curriculum vitae and a cover letter explaining your interest in the position to: Ronan M.T. Fleming, email: