The University of Manchester
Manchester, Lancashire
Duration: 3 years fixed term About the role: We are seeking a Postdoctoral Scientist to join the newly established Myeloid Cancer Biology group, led by Dr Justin Loke, to investigate how inherited and acquired genetic mutations cooperate to drive acute myeloid leukaemia (AML) and myelodysplastic syndrome (MDS). A key focus of the post are inherited conditions caused by germline mutations that carries a substantial lifetime risk of AML/MDS. How specific combinations of somatic mutations cooperate with these inherited mutations to drive leukaemic transformation remains poorly understood, in large part because existing model systems cannot recreate the complex, multi-mutation genotypes seen in patients. The post holder will use a novel transgenic mouse platform developed by the PI and colleagues, in which Cas12a-mediated genome editing enables simultaneous disruption of up to four genetic loci within single haematopoietic stem and progenitor cells. Using this platform, the post holder will generate and characterise genetically complex murine models of inherited predisposition related MDS/AML, combining in vivo transplantation experiments, flow cytometry and histopathology with single-cell multiome (RNA and ATAC) profiling to define the epigenetic programmes underlying malignant transformation, and to establish proof-of-concept for novel therapeutic vulnerabilities in this and related inherited myeloid malignancy syndromes. This post is supported by a John Goldman Fellowship from Leukaemia UK. The Environment: The Myeloid Cancer Biology group was established in October 2025, and this post offers an early opportunity to help shape a new laboratory's research programme and infrastructure. The post holder will work closely with the Group Leader and benefit from the wider myeloid malignancy and haematology community at the Institute, including collaboration with Professor Tim Somervaille (epigenetic mechanisms in myeloid neoplasia) and Professor Georges Lacaud (RUNX1 biology and haematopoiesis), aswell as an established international collaboration on Cas12a genome-editing technology. The Institute's core facilities - including the Genome Editing and Mouse Models Facility, Flow Cytometry, Molecular Biology and Computational Biology Support - provide direct technical support for the in vivo and single-cell components of this project. The group is based in the Paterson Building, directly attached to The Christie NHS Foundation Trust, providing a translational research environment with strong links to clinical haematology. About you: You should have, or be near completion of, a PhD in cancer biology, molecular biology, genetics, immunology, haematology or a related field. You will have demonstrable practical experience of mammalian cell culture and core molecular biology techniques, together with a track record of scientific publication or presentation, and the ability to define and solve research questions independently. Prior experience with murine/in vivo models, genome engineering or CRISPR-based approaches, and/or single-cell genomic technologies would be a strong advantage, as would a background in haematopoiesis or leukaemia biology - but candidates with strong complementary expertise and a genuine interest in developing these skills are also encouraged to apply. Closing date: 18:00, 11 October 2026 Sunday First-round interviews (via Microsoft Teams): 15 and 16 October 2026 Second-round interviews (in person): w/c 26 October 2026
Duration: 3 years fixed term About the role: We are seeking a Postdoctoral Scientist to join the newly established Myeloid Cancer Biology group, led by Dr Justin Loke, to investigate how inherited and acquired genetic mutations cooperate to drive acute myeloid leukaemia (AML) and myelodysplastic syndrome (MDS). A key focus of the post are inherited conditions caused by germline mutations that carries a substantial lifetime risk of AML/MDS. How specific combinations of somatic mutations cooperate with these inherited mutations to drive leukaemic transformation remains poorly understood, in large part because existing model systems cannot recreate the complex, multi-mutation genotypes seen in patients. The post holder will use a novel transgenic mouse platform developed by the PI and colleagues, in which Cas12a-mediated genome editing enables simultaneous disruption of up to four genetic loci within single haematopoietic stem and progenitor cells. Using this platform, the post holder will generate and characterise genetically complex murine models of inherited predisposition related MDS/AML, combining in vivo transplantation experiments, flow cytometry and histopathology with single-cell multiome (RNA and ATAC) profiling to define the epigenetic programmes underlying malignant transformation, and to establish proof-of-concept for novel therapeutic vulnerabilities in this and related inherited myeloid malignancy syndromes. This post is supported by a John Goldman Fellowship from Leukaemia UK. The Environment: The Myeloid Cancer Biology group was established in October 2025, and this post offers an early opportunity to help shape a new laboratory's research programme and infrastructure. The post holder will work closely with the Group Leader and benefit from the wider myeloid malignancy and haematology community at the Institute, including collaboration with Professor Tim Somervaille (epigenetic mechanisms in myeloid neoplasia) and Professor Georges Lacaud (RUNX1 biology and haematopoiesis), aswell as an established international collaboration on Cas12a genome-editing technology. The Institute's core facilities - including the Genome Editing and Mouse Models Facility, Flow Cytometry, Molecular Biology and Computational Biology Support - provide direct technical support for the in vivo and single-cell components of this project. The group is based in the Paterson Building, directly attached to The Christie NHS Foundation Trust, providing a translational research environment with strong links to clinical haematology. About you: You should have, or be near completion of, a PhD in cancer biology, molecular biology, genetics, immunology, haematology or a related field. You will have demonstrable practical experience of mammalian cell culture and core molecular biology techniques, together with a track record of scientific publication or presentation, and the ability to define and solve research questions independently. Prior experience with murine/in vivo models, genome engineering or CRISPR-based approaches, and/or single-cell genomic technologies would be a strong advantage, as would a background in haematopoiesis or leukaemia biology - but candidates with strong complementary expertise and a genuine interest in developing these skills are also encouraged to apply. Closing date: 18:00, 11 October 2026 Sunday First-round interviews (via Microsoft Teams): 15 and 16 October 2026 Second-round interviews (in person): w/c 26 October 2026
The University of Manchester
Manchester, Lancashire
Job Title: People Partners Location: Oxford Road, Manchester Salary: £47,389 - £58,225 per annum depending on experience Job Type: Full time (1 FTE) - Permanent Closing Date: 25/09/2026 Join us as a People Partner at the University of Manchester. We're looking for three People Partners to play a pivotal role within our People Directorate, working closely with Faculties and Professional Services to deliver high-quality, consistent people solutions. Acting as a trusted advisor and coach to senior leaders, you will shape and deliver initiatives across talent, succession, organisational and workforce design, development, performance, and culture, helping to build inclusive, high-performing environments. You'll partner with Schools, Professional Services Directorates or key functions to understand priorities, diagnose organisational needs, and connect them with the expertise of our Centres of Excellence, ensuring impactful solutions are embedded effectively. If you're an experienced HR professional who thrives on influence, collaboration and driving meaningful change, we'd love to hear from you. What you will get in return: Fantastic market leading Pension scheme Excellent employee health and wellbeing services including an Employee Assistance Programme Exceptional starting annual leave entitlement, plus bank holidays Additional paid closure over the Christmas period Local and national discounts at a range of major retailers 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. Our University is positive about flexible working. Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies. Any CV's submitted by a recruitment agency will be considered a gift. This vacancy will close for applications at midnight on the closing date. Please click APPLY to be redirected to our website to complete an application form. Candidates with experience of; Personnel Officer, Human Resources Officer, HR Officer, HR Executive, Personnel Manager, Personnel Development, People Development, Staff Development Officer may also be considered for this role.
Job Title: People Partners Location: Oxford Road, Manchester Salary: £47,389 - £58,225 per annum depending on experience Job Type: Full time (1 FTE) - Permanent Closing Date: 25/09/2026 Join us as a People Partner at the University of Manchester. We're looking for three People Partners to play a pivotal role within our People Directorate, working closely with Faculties and Professional Services to deliver high-quality, consistent people solutions. Acting as a trusted advisor and coach to senior leaders, you will shape and deliver initiatives across talent, succession, organisational and workforce design, development, performance, and culture, helping to build inclusive, high-performing environments. You'll partner with Schools, Professional Services Directorates or key functions to understand priorities, diagnose organisational needs, and connect them with the expertise of our Centres of Excellence, ensuring impactful solutions are embedded effectively. If you're an experienced HR professional who thrives on influence, collaboration and driving meaningful change, we'd love to hear from you. What you will get in return: Fantastic market leading Pension scheme Excellent employee health and wellbeing services including an Employee Assistance Programme Exceptional starting annual leave entitlement, plus bank holidays Additional paid closure over the Christmas period Local and national discounts at a range of major retailers 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. Our University is positive about flexible working. Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies. Any CV's submitted by a recruitment agency will be considered a gift. This vacancy will close for applications at midnight on the closing date. Please click APPLY to be redirected to our website to complete an application form. Candidates with experience of; Personnel Officer, Human Resources Officer, HR Officer, HR Executive, Personnel Manager, Personnel Development, People Development, Staff Development Officer may also be considered for this role.
The University of Manchester
City, Manchester
Qualification Type: PhD Location: Manchester - UK Funding for: UK and International Funding amount: £21,805 per annum Start date: September 2026 Hours: Full Time Closes: 29 May 2026 (midnight) PhD by Enterprise (Alliance Manchester Business School) The University of Manchester's PhD by Enterprise is a new four year doctoral programme that combines world class research with structured entrepreneurship training. The programme enables the University's research portfolio to generate tangible economic, environmental and societal impact through venture creation and enterprise-led pathways. The programme includes a fully funded studentship to commence in September 2026, covering tuition fees, UKRI stipend (2026/27 rate £21,805 per annum) and Research Training Support Grant. You will be based in the Alliance Manchester Business School at The University of Manchester, a top 5 UK business school (QS World University Rankings 2026). Project details: AIDE: Agentic Intelligence for Decision-making in Investment and Enterprise Investment and venture evaluation environments, such as venture capital, private equity, and university innovation ecosystems, are becoming increasingly data intensive. Yet despite the abundance of available information, decision-making across deal sourcing, evaluation, due diligence, and post investment monitoring remains fragmented and highly manual. Current commercial platforms excel at search and data aggregation, but they provide limited support for deeper reasoning, scenario exploration, or coordinated, lifecycle wide decision support. This PhD project, AIDE: Agentic Intelligence for Decision-making in Investment and Enterprise, aims to address these challenges by developing next-generation AI systems capable of supporting holistic, data-driven and uncertainty-aware decision-making. Based in the prestigious Alliance Manchester Business School, the project will also explore the design and development of knowledge graphs to structure and connect heterogeneous data sources, enabling richer contextual understanding and reasoning. The project offers an exciting opportunity to work at the frontier of applied AI, decision sciences, and real-world innovation ecosystems, advancing new research while contributing to a potential future commercial venture. A central ambition of the project is to build AI systems that are not only powerful, but also explainable. Investment decisions are high-stakes, and users must be able to understand why the system recommends particular actions or highlights certain risks. The PhD will explore explainable AI (XAI) methods that enable transparency, interpretability and user trust, ensuring that recommendations can be interrogated, justified, and adapted by human experts. This includes surfacing the key evidence, assumptions, and uncertainties underpinning each step of the decision process, potentially leveraging knowledge graph structures to trace relationships and reasoning paths across data. The research will investigate how diverse information sources, such as structured financial data, textual documents, company disclosures, and online signals, can be integrated into unified representations that support robust reasoning, including the construction and utilisation of knowledge graphs for entity linking, relationship modelling, and semantic integration. Equally important is modelling uncertainty: decision-makers often work with incomplete, noisy or fast-changing data. The project will examine techniques for quantifying and propagating uncertainty across multi-stage workflows, enabling users to explore how assumptions or market changes affect potential outcomes. The student will also study how multiple AI agents can collaborate to reflect real-world investment workflows, coordinating tasks such as screening, due-diligence analysis, risk assessment and scenario modelling, with knowledge graphs potentially serving as a shared structured memory and coordination layer across agents. The design will emphasise human-AI collaboration, ensuring users retain oversight, agency, and the ability to challenge or override recommendations. Methodologically, the project blends machine learning, probabilistic modelling, multi-agent systems, explainable AI, and human-computer interaction, alongside knowledge representation and graph-based reasoning techniques. A design-science research approach will be used, with iterative prototyping, evaluation using realistic scenarios, and engagements with practitioners from investment and innovation communities. Academic Criteria: Bachelor's (Honours) degree at 2:1 or above (or overseas equivalent); and Master's degree in a relevant cognate subject normally with an overall average of 65% or above (or equivalent) Professional qualifications and/or relevant and appropriate experience. Desirable Criteria: A degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, Statistics, Mathematics, Engineering, Information Systems, or a closely related discipline. A Master's degree in one of the above areas. Strong analytical and programming skills (e.g., Python, machine learning frameworks) are advantageous, alongside an interest in applied AI, decision making systems, and explainable or uncertainty aware modelling. Candidates from numerate disciplines with professional experience in data science, analytics, financial technology, investment analysis, or innovation ecosystems are also encouraged. Crucially, applicants should be motivated to conduct high quality research at the intersection of AI and real world enterprise applications, with an interest in developing transparent, explainable and user centred decision support technologies. English Language Evidence: IELTS minimum scores - 7.0 overall, 6.5 other sections. Other tests may be considered. TOEFL (internet based) test minimum scores - 100 overall, 25 in all sections. Pearson Test of English (PTE) UKVI/SELT or PTE Academic minimum scores - 76 overall, 76 in writing, 70 in other sections. To demonstrate that you have taken an undergraduate or postgraduate degree in a majority English speaking nation within the last 5 years. Other tests may be considered. The application deadline will be 11:59PM (GMT) on 29/05/26. Apply online for 'PhD by Enterprise HUMS'. If you would like to discuss the project further, contact Prof Richard Allmendinger ()
Qualification Type: PhD Location: Manchester - UK Funding for: UK and International Funding amount: £21,805 per annum Start date: September 2026 Hours: Full Time Closes: 29 May 2026 (midnight) PhD by Enterprise (Alliance Manchester Business School) The University of Manchester's PhD by Enterprise is a new four year doctoral programme that combines world class research with structured entrepreneurship training. The programme enables the University's research portfolio to generate tangible economic, environmental and societal impact through venture creation and enterprise-led pathways. The programme includes a fully funded studentship to commence in September 2026, covering tuition fees, UKRI stipend (2026/27 rate £21,805 per annum) and Research Training Support Grant. You will be based in the Alliance Manchester Business School at The University of Manchester, a top 5 UK business school (QS World University Rankings 2026). Project details: AIDE: Agentic Intelligence for Decision-making in Investment and Enterprise Investment and venture evaluation environments, such as venture capital, private equity, and university innovation ecosystems, are becoming increasingly data intensive. Yet despite the abundance of available information, decision-making across deal sourcing, evaluation, due diligence, and post investment monitoring remains fragmented and highly manual. Current commercial platforms excel at search and data aggregation, but they provide limited support for deeper reasoning, scenario exploration, or coordinated, lifecycle wide decision support. This PhD project, AIDE: Agentic Intelligence for Decision-making in Investment and Enterprise, aims to address these challenges by developing next-generation AI systems capable of supporting holistic, data-driven and uncertainty-aware decision-making. Based in the prestigious Alliance Manchester Business School, the project will also explore the design and development of knowledge graphs to structure and connect heterogeneous data sources, enabling richer contextual understanding and reasoning. The project offers an exciting opportunity to work at the frontier of applied AI, decision sciences, and real-world innovation ecosystems, advancing new research while contributing to a potential future commercial venture. A central ambition of the project is to build AI systems that are not only powerful, but also explainable. Investment decisions are high-stakes, and users must be able to understand why the system recommends particular actions or highlights certain risks. The PhD will explore explainable AI (XAI) methods that enable transparency, interpretability and user trust, ensuring that recommendations can be interrogated, justified, and adapted by human experts. This includes surfacing the key evidence, assumptions, and uncertainties underpinning each step of the decision process, potentially leveraging knowledge graph structures to trace relationships and reasoning paths across data. The research will investigate how diverse information sources, such as structured financial data, textual documents, company disclosures, and online signals, can be integrated into unified representations that support robust reasoning, including the construction and utilisation of knowledge graphs for entity linking, relationship modelling, and semantic integration. Equally important is modelling uncertainty: decision-makers often work with incomplete, noisy or fast-changing data. The project will examine techniques for quantifying and propagating uncertainty across multi-stage workflows, enabling users to explore how assumptions or market changes affect potential outcomes. The student will also study how multiple AI agents can collaborate to reflect real-world investment workflows, coordinating tasks such as screening, due-diligence analysis, risk assessment and scenario modelling, with knowledge graphs potentially serving as a shared structured memory and coordination layer across agents. The design will emphasise human-AI collaboration, ensuring users retain oversight, agency, and the ability to challenge or override recommendations. Methodologically, the project blends machine learning, probabilistic modelling, multi-agent systems, explainable AI, and human-computer interaction, alongside knowledge representation and graph-based reasoning techniques. A design-science research approach will be used, with iterative prototyping, evaluation using realistic scenarios, and engagements with practitioners from investment and innovation communities. Academic Criteria: Bachelor's (Honours) degree at 2:1 or above (or overseas equivalent); and Master's degree in a relevant cognate subject normally with an overall average of 65% or above (or equivalent) Professional qualifications and/or relevant and appropriate experience. Desirable Criteria: A degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, Statistics, Mathematics, Engineering, Information Systems, or a closely related discipline. A Master's degree in one of the above areas. Strong analytical and programming skills (e.g., Python, machine learning frameworks) are advantageous, alongside an interest in applied AI, decision making systems, and explainable or uncertainty aware modelling. Candidates from numerate disciplines with professional experience in data science, analytics, financial technology, investment analysis, or innovation ecosystems are also encouraged. Crucially, applicants should be motivated to conduct high quality research at the intersection of AI and real world enterprise applications, with an interest in developing transparent, explainable and user centred decision support technologies. English Language Evidence: IELTS minimum scores - 7.0 overall, 6.5 other sections. Other tests may be considered. TOEFL (internet based) test minimum scores - 100 overall, 25 in all sections. Pearson Test of English (PTE) UKVI/SELT or PTE Academic minimum scores - 76 overall, 76 in writing, 70 in other sections. To demonstrate that you have taken an undergraduate or postgraduate degree in a majority English speaking nation within the last 5 years. Other tests may be considered. The application deadline will be 11:59PM (GMT) on 29/05/26. Apply online for 'PhD by Enterprise HUMS'. If you would like to discuss the project further, contact Prof Richard Allmendinger ()
