Applications are invited for a Research Associate position as part of a multidisciplinary research project funded by EPSRC aims to tackle a fundamental and yet industrially relevant problem to develop a near isothermal co-generation compressed air energy storage (CAES) system. The aim is to perform thermodynamic and experimental studies to design and build the near-isothermal co-generation CAES system using liquid piston compressor/expander technology.  The proposed multidisciplinary project brings together researchers from the University of Manchester, Imperial College London and University of Birmingham. Therefore, the Research Associate will join a dynamic research team comprising five academics, four postdoctoral researchers, and five PhD students focusing on various aspects of the compressed air energy storage technologies. The Research Associate will have close engagement with 18 industrial partners, including EDF-Energy, Arup and Siemens-Energy.

We invite applications for the position of Post-Doctoral Research Associate to join the team on a 5 year, £3.2M, NERC Large Grant “Feedbacks between mineral reactions and mantle convection”, a project to shed new light on how mineral reactions influence the positions of major seismic discontinuities in Earth’s mantle. Key hypotheses to be explored are: the “dynamic pressure” in the mantle has an effect on mineral reaction and hence the positions of seismic discontinuities, and that stress has a major effect on reaction conditions and again on the positions of seismic discontinuities.

Your role will be to perform experiments at mantle conditions (up to 25 GPa, temperatures up to 1800°C) measuring the effect of stress on evolving mineral properties. These experiments will in volve both stress relaxation and controlled strain-rate deformation experiments, in Manchester and at national facilities. To understand how the minerals change, we will examine the experimental products to discover the details of structure and chemistry on the scale of individual grains.

The observations gained from these experiments will enable us to understand how chemistry and stress interact and their effect on the depth of the mantle phase transitions. You will work closely with other team members (Liverpool, UCL, Orleans, Cardiff) to ground-truth their modelling effort. The whole team will contribute to comparing these results to seismological observations (Cambridge).

A PhD (or equivalent) in Computer Science, Robotics, Artificial Intelligence or allied disciplines is required. Excellent programming skills are essential, together with a strong publication track record and robotics and/or machine learning skills. Previous experience of research projects on human-robot collaboration or cognitive robotics is a desirable skill.

The research fellows will be working collaboratively as part of the Cognitive Robotics Lab and the Manchester Centre for Robotics and AI, under the supervision of Professor Angelo Cangelosi. Close collaboration with the other project partners will also be required.

We wish to appoint an enthusiastic and motivated scientist to work on the ERC funded Actinide molecules for exploring the frontiers of fundamental physics (ActMol) led by Prof Kieran Flanagan. The research activity is based at Photon Science Institute in Manchester. The successful candidate will assist in the development, testing and commissioning of novel cryogenic ion traps,  mass spectrometry and laser spectroscopy instrumentation. They will support experiments, analyse results and preparing papers for publication.

The successful candidate will possess a Ph.D. or equivalent in a relevant field of nuclear physics. The ability to work as part of an team, both in the UK and abroad, is essential. Knowledge of experimental nuclear research using ion traps and lasers spectroscopy with low-energy radioactive ion beams is essential.

As an equal opportunities employer we welcome applicants from all sections of the community regardless of age, sex, gender (or gender identity), ethnicity, disability, sexual orientation and transgender status.  All appointments are made on merit.

The University of Manchester has a strong history of research into marine renewable energy with leading international activity. In this project, we are here concerned with control of onshore wave energy converters to maximise power capture and integrate them into a standalone microgrid to provide power for remote islands and rural areas. This collaborative project is funded by the Innovate UK Energy Catalyst Round 10. This is a multi-disciplinary project involving a large consortium consisting of leading companies and researchers in this field, with cross-disciplinary expertise in wave energy converter designs, hydrodynamics, control, power electronics, sea wave prediction, and validation by hardware in the loop testing experiments. This 18-month post focuses on the control-oriented modelling, controller design, co-design and real-time control experiment by HIL.

We are looking for an enthusiastic and motivated research associate looking for a challenging opportunity in control system designs for renewable energy systems, who can communicate and work collaboratively with the industrial and academic partners in the consortium. The applicant will have a PhD in a relevant degree and/or relevant equivalent experience. Relevant theoretical and practical experience of modelling and controller design will be essential.

A Postdoctoral Research Associate (PDRA) position is available for an outstanding and ambitious high voltage engineer to undertake research in the development of high voltage dynamic DC cable applications. The research is a Horizon Europe project coordinated by the University of Manchester and in close collaboration with ETH Zurich, University of Vienna, Universitat Politècnica de Catalunya, NKT Cable Group, Shell France, Shell Research Ltd, S&B Insurance Advisors, Arttic Innovation. This project is part of a rapidly growing research area at the University of Manchester on high voltage transmission cables to deliver a resilient network for net-zero.

The position will be held in the High Voltage Research Group. You will be developing a new generation of filled polymeric materials for applications high voltage cables. One of the key missions of this project is to provide transformative research and knowledge on how DC dynamic cable insulation systems are assessed in the future. Within this context, you will work on the design and development of a high voltage DC testing circuit to investigate the various parameters that could negatively impact the reliability of DC dynamic cable in representative environmental conditions. Through experimental tests, a cable lifetime model will be developed. You will be based in the Power and Energy Division of the EEE School and should possess a PhD in a relevant STEM discipline. You will work within the High Voltage Research Group at UNIMAN and collaborate with scientists and engineers from other partners of the DCDYNAMIC project. In addition to publishing high quality journal articles, you will be working with industry partners such as NKT Cable Group to translate the science into industrial applications.

You will have experience in high voltage engineering and cable insulation systems. You should be able to maintain a high degree of accuracy in your research, and to write and present your work both internally and for wider dissemination.  You should have a PhD in Electrical Engineering or a related discipline and an emergent publication track record.

You will be part of a world-leading team working on development of high voltage DC dynamic cable technology, across the Henry Royce Institute, National Graphene Institute and High Voltage Laboratories.

Applicants are invited for the posts of Postdoctoral Research Associate or Research Fellow in Machine Learning to work with AI Researchers in the Centre for AI Fundamentals at the University of Manchester.

You will join a team of probabilistic modellers and machine learning researchers developing new collaborative AI principles and methods. This is an exciting topic which inspires new problems in fundamental ML work, and allows attacking new applications which make a difference, for instance in scientific research.  Different team members have different expertise, and by working together are able to address more novel problems. Keywords include: automatic experimental design, Bayesian inference, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, inverse reinforcement learning, computational rationality and user modelling, and simulator-based inference.

The post-holder will work on a project from Professor Kaski’s UKRI Turing AI World-Leading Fellowship. Research here includes work to develop new principles and methods for Advanced User Modelling, sequential decision making and Automatic Experimental Design, with and without a Human-in-the-Loop. We will collaborate with researchers from ELLIS, the Alan Turing Institute and FCAI.

A PhD in a relevant discipline is required.  You should have excellent organisational skills and the ability to work well both in a team and using your own initiative. Experience in research methods and techniques to work within established research programmes is essential.

A Research Associate is required in the Department of Chemistry, University of Manchester to synthesise and characterise organic materials as part of the BBSRC Prosperity Partnership SUCCEED. The project focusses on the development of new routes from bioderived feedstocks to commodity chemicals and sustainable fuels.  This post will involve collaboration with researchers within the University and in the company partner. Applicants should have a strong track record in organic synthesis with experience in process, continuous or scale up chemistry.

A postdoctoral position is available for 2 years, with a possible extension of another 2 years, through UKRI funding underwriting a European Research Council (ERC) Advanced Grant. This position is open to an outstanding and ambitious computational scientist with an interest in the design of machine learning potentials for biomolecular simulation.

By its novel architecture, the fully in-house developed force field FFLUX aims to make a step change in the reliability of modelling of peptides/proteins in aqueous solution.

The machine learning method Gaussian Process Regression is used to create knowledgeable quantum atoms that accurately predict energies and multipole moments.

The post-holder will develop probabilistic machine learning approaches for one or more of these data modalities. In the context of biology there are several challenges to applying generative models, e.g. uncovering causal relationships, incorporating prior knowledge, dealing with experimental variability, and fine-tuning of pre-trained models for specific applications. In this project we are particularly interested in using models to make decisions, e.g. to design further experiments, in which case it is important to associate model-based inferences with well-calibrated uncertainties.

The post is part of the UKRI-funded “AI hub in generative models” involving seven Universities and several partner organizations. The overall aim of the hub is to develop tools that industry, science and government can use to build responsible generative models to benefit the economy and society.

The successful applicant will have a PhD with a significant computational and/or statistical element and will have recent experience of probabilistic modelling and/or machine learning. A strong interest in biology is essential. The candidate should have a good scientific publication record given career stage and be self-motivated, hard-working and able to work in a team.

We seek outstanding candidates with a strong academic background who will be comfortable engaging with the vibrant cross-disciplinary community of FinTech across the School and the University. The ideal candidate will be able to demonstrate commitment to produce original and innovative scholarly work and publish at top academic journals in their discipline and at the same time be comfortable in a business setting.

The candidate should also be able to work with students in mentoring around the School’s FinTech Centre, design new research projects and help in existing ones. Alliance MBS plans to strengthen FinTech research and thus the above characteristics and the ones described in the “person specification” section below will be invaluable to this initiative.

As an equal opportunities employer we welcome applicants from all sections of the community regardless of age, sex, gender (or gender identity), ethnicity, disability, sexual orientation and transgender status.  All appointments are made on merit.

We are seeking to recruit a Research Associate to undertake research and project management duties for the 5-year GENERATE project (an ERC/UKRI Consolidator grant, see https://www.manchester.ac.uk/about/news/ukri-award-the-university-of-manchester-17m-to-investigate-gendered-energy-inequalities/).

The appointee will be tasked with ensuring that the project is progressing in line with anticipated schedules and deliverables, while liaising with relevant partners and stakeholders. The Research Associate will undertake, in the main, qualitative data collection and analysis (desk-based document collection, interviews, ethnographies, governance mapping) on gender and energy inequalities, with a focus on the Western Balkans. The post holder will also be tasked with preparing and writing project reports, peer-reviewed publications and follow-on funding applications, in addition to undertaking non-academic dissemination activities linked to the project.

The long-held tradition of studying microorganisms in isolation has, until recently, obscured the fact that microbial life exists as interdependent multi-species assemblages. Despite the profound impact microbial communities have on their environment and the technological advances in microbial genomics in recent years, the rules that govern the structure, function and stability of microbial communities are complex and only partially understood. Although a plethora of mechanisms that influence microbial communities have been identified, from metabolic priming, to inter- and intraspecific metabolic signalling and even direct antibiosis, holistic understanding has yet to be achieved in any microbial system. In this project we will develop a novel experimental system that is complex yet tractable, enabling rich, coordinated multi-omics analysis coupled with computational modelling to deliver deep mechanistic insight. We will apply a cutting-edge toolbox of experimental, analytical, and computational approaches to reveal the rules of life in this system that will serve as a blueprint to apply to any microbiome.

You will be responsible for conducting research under the supervision of Prof Nixon to establish and characterise the microbial communities from hot springs that successfully grow on CO2 and H2 in laboratory-controlled experiments. You will be responsible for generating, analysing and coordinating the publication of results from amplicon, metagenomic and metatranscriptomic sequencing data. These data will be generated from field samples, batch cultivation experiments and continuous cultivation studies using flow through bioreactors. You will work closely with other postdocs and PhD students in this project, who will be applying complementary approaches (including proteomics and metabolomics, as well as multi-species computational modelling), and will be expected to join at least one fieldtrip to Iceland with other sLoLa team members. Your experimental and analytical work will be supported by a full-time technical specialist.

The long-held tradition of studying microorganisms in isolation has, until recently, obscured the fact that microbial life exists as interdependent multi-species assemblages. Despite the profound impact microbial communities have on their environment and the technological advances in microbial genomics in recent years, the rules that govern the structure, function and stability of microbial communities are complex and only partially understood. Although a plethora of mechanisms that influence microbial communities have been identified, from metabolic priming, to inter- and intraspecific metabolic signalling and even direct antibiosis, holistic understanding has yet to be achieved in any microbial system. In this project we will develop a novel experimental system that is complex yet tractable, enabling rich, coordinated multi-omics analysis coupled with computational modelling to deliver deep mechanistic insight. We will apply a cutting-edge toolbox of experimental, analytical, and computational approaches to reveal the rules of life in this system that will serve as a blueprint to apply to any microbiome.

You will be responsible for successfully conducting a computational work package focusing on the design and implementation of multi-species metabolic models. You will develop genome-resolved metabolic models from multi-omics (especially metagenomic) data to make mechanism-based predictions about the functional response of bacterial communities to environmental variation, changes in species composition and experimental intervention, and use these predictions to develop testable hypotheses about the rules of life determining community function and composition. You will work closely with other postdocs and PhD students in this project, who will be applying complementary approaches (including metagenomics, metatranscriptomics, proteomics and metabolomics) and generating the data you will use to create and validate the multi-species metabolic models.

This project is led by Professor Sophie Nixon at the University of Manchester, in collaboration with the Earlham Institute. In this project we are interrogating the rules that govern microbiomes that inhabit terrestrial hot springs.

The long-held tradition of studying microorganisms in isolation has, until recently, obscured the fact that microbial life exists as interdependent multi-species assemblages. Despite the profound impact microbial communities have on their environment and the technological advances in microbial genomics in recent years, the rules that govern the structure, function and stability of microbial communities are complex and only partially understood. Although a plethora of mechanisms that influence microbial communities have been identified, from metabolic priming, to inter- and intraspecific metabolic signalling and even direct antibiosis, holistic understanding has yet to be achieved in any microbial system. In this project we will develop a novel experimental system that is complex yet tractable, enabling rich, coordinated multi-omics analysis coupled with computational modelling to deliver deep mechanistic insight. We will apply a cutting-edge toolbox of experimental, analytical, and computational approaches to reveal the rules of life in this system that will serve as a blueprint to apply to any microbiome.

Immune-mediated inflammatory diseases (IMIDs) include conditions affecting the skin, joints, and/or the gut. They lead to a big burden on the body and mind for 7% of people globally. New high-cost treatments that target inflammation-controlling proteins have transformed the lives of people with IMIDs. When deciding whether to take these treatments, patients and their doctors rely on evidence. This includes information from randomized controlled trials (RCTs), where two or more different medicines, of which one may be a dummy medicine, are given randomly to groups of participants. There are also observational studies, where large groups of people are observed in routine settings without being allocated medicines. Observational studies are representative, large, and relatively cheap but can be biased. RCTs are more reliable but people from underserved populations, such as people from lower socio-economic backgrounds, are often excluded or under-represented. Only using evidence from RCTs to guide treatment decisions could introduce privileges or disadvantages for different groups of people, leading to an overestimation of benefits and an underestimation of risks overall.

This project, entitled “Mastering Effectiveness Research by Meta-Analysing Trial and Real World Evidence in Immune-mediated inflammatory Diseases (MERMAID)”, aims to use RCTs and observational studies together to find better ways of using current IMID treatments and test treatment effects in underserved populations.