Loughborough University

Ref.: 534998 Work type: Full-time Department: School of Biomedical Sciences (22600) Categories: Senior Research Staff & Post-doctoral Fellow Research Assistant Professor in the School of Biomedical Sciences (Ref.:534998) Professor Guoping Fan’s lab invites applications from highly motivated and experienced postdoctoral researchers for a Research Assistant Professorship. This position is ideal for ambitious scientists seeking to transition to independent research leadership while contributing to cutting-edge, interdisciplinary projects at the intersection of AI Virtual Cell modeling, Epigenetics, and Stem Cell biology. Research Focus for This Position We are particularly interested in candidates who can integrate AI-driven virtual cell approaches with experimental epigenetics and stem cell platforms. Potential projects include: Developing AI/multimodal models (e.g., integrating single-cell omics, spatial transcriptomics, epigenomics, and proteomics) to simulate virtual neural stem cells or organoids. Investigating epigenetic regulation (DNA methylation, histone modifications) in stem cell fate decisions, reprogramming, and disease modeling using AI predictive tools. Combining computational virtual cell frameworks with wet-lab validation in human iPSC-derived neural models, organoids, or in vivo systems. Exploring translational applications, such as AI-guided mRNA therapies or precision modeling of neurodevelopmental disorders. This role offers the opportunity to lead independent projects, mentor students, secure external funding, and publish in top-tier journals while benefiting from the lab’s expertise and resources. Requirements PhD in Biomedical Sciences, Bioinformatics, Stem Cell Biology, Epigenetics, Computational Biology, or a closely related field. At least 3–5 years of postdoctoral experience with a strong publication record (first-author papers in high-impact journals preferred). Demonstrated expertise in at least two of the following areas: AI/machine learning for biological modeling (e.g., virtual cell, foundation models, graph neural networks, or multimodal omics integration). Epigenetics (DNA methylation, chromatin analysis, single-cell epigenomics). Stem cell research (iPSC culture, differentiation, organoids, neural models). Experience with CRISPR, organoid technology, or mRNA therapeutics is a plus. Proven ability to work independently and collaboratively in an interdisciplinary setting. Excellent communication skills in English.  What We Offer The appointment will commence as soon as possible on a 3-year fixed-term basis, with the possibility of renewal subject to satisfactory performance and funding availability.  A highly competitive salary commensurate with qualifications and experience will be offered. The appointment on fixed terms will attract a contract-end gratuity and University contribution to a retirement benefits scheme, totaling up to 15% of basic salary. Other benefits include annual leave, medical benefits and free access to on-campus gyms and libraries. How to Apply The University only accepts online application for the above post. Applicants should apply online at the University’s careers site (https://jobs.hku.hk) and upload (1) Cover letter outlining research interests, relevant experience, and fit for the position (2) an up-to-date C.V. including full publication list (3) Contact information for 3 references (4) 1–2 page research proposal on how you would integrate AI virtual cell approaches with epigenetics/stem cells in the lab. Review of applications will start from April 30, 2026 and continue until October 29, 2026, or until the post is filled, whichever is earlier. The University is an equal opportunities employer and is committed to equality, ethics, inclusivity, diversity and transparency

University of Helsinki, Faculty of Medicine invites applications for the position of  Doctoral Researcher  in the Cancer Data Science group led by Associate Professor Mariike Kuijjer, at the iCAN Flagship in Digital Precision Cancer Medicine at the Faculty of Medicine, University of Helsinki. The position is funded through a project by the Sigrid Jusélius Foundation titled “Regulatory determinants of metastatic progression in high-grade serous ovarian cancer.” The ideal starting date is summer or early fall 2026, but this is flexible and can be discussed. The appointment is a full-time position and is made for a period of three years with possible extension depending on future funding. Our research The Kuijjer group was established in 2018 at the University of Oslo and expanded to the University of Helsinki in 2025. The group focuses on developing computational and data science approaches to increase our understanding of gene regulation and how rewiring of gene regulatory networks can influence cancer development, progression, and disease heterogeneity. The group's current projects use network science and artificial intelligence to develop tools that (i) integrate multi-modal data, (ii) model gene regulation at increased genomic and cellular resolution, (iii) refine the analysis of genome-wide regulatory networks. The group applies these tools to study various cancer types, including ovarian cancer. For more information, visit kuijjerlab.org. About the roleThe advertised project aims to identify regulatory determinants of ovarian cancer progression through regulatory network modeling and analysis. The selected candidate will apply and extend network modeling approaches developed in the Kuijjer group to model patient- and cell type-specific gene regulatory networks using bulk and single-cell ovarian cancer data. These networks will be analyzed to define patient-specific regulatory heterogeneity in primary ovarian cancer, identify regulatory rewiring that drives metastatic progression, characterize the regulatory mechanisms modulated by therapeutic pressure, and assess how these programs relate to therapy response. The project will leverage various publicly available datasets as well as data from the in-house Oncosys-OVA study led by our collaborator Prof. Anniina Färkkilä, University of Helsinki. Overall, the project combines methodological development with biological discovery, offering opportunities to contribute both to computational innovation and to mechanistic insights into ovarian cancer progression. The candidate We seek a highly motivated candidate with strong computational skills in the field of high-throughput omics data analysis, comparative genomics, functional genomics, or related fields. The candidate is excited about applying computational tools to answer questions in cancer research. They are collaborative and creative, and have strong programming skills dedicated to the analysis of large-scale genomics data. Experience with the analysis of omics data is required for this position. Knowledge of cancer biology is a strong advantage. Experience with high-performance computing, genomic data integration, biological network analysis, and/or gene regulation is desirable, but not required.  The position is open to applicants with a Master's degree in computational biology, bioinformatics, biostatistics, cancer genomics, network science, or related fields. Candidates who are finishing their Master's thesis are also welcome to apply. They should mention the expected date of their defense and steps that need to be completed to finish their thesis in the cover letter. Qualification requirements Master's degree in computational biology, bioinformatics, biostatistics, cancer genomics, network science, or a related field. Applicants with a background in biology or (bio)medicine are welcome to apply, provided they have documented expertise in computational biology Proficiency in programming (such as Python, R, Bash) Experience with analysis of high-throughput omics data is required Knowledge of cancer biology is a strong advantage Experience with genomic data integration, biological network analysis, and/or gene regulation is an advantage Experience with high-performance computing is desirable Strong analytical and problem-solving skills Teamwork skills and the willingness to collaborate, share ideas, and contribute to a multidisciplinary research environment spanning Helsinki and Oslo Strong communication skills in English, with both written and verbal proficiency The appointee should either already have the right to pursue a doctoral degree at the University of Helsinki by the start of the appointment or apply for the right and obtain it within the probationary period of six months of their appointment. If the candidate does not already have the right to pursue a doctoral degree at the University of Helsinki, it must be applied for separately: www.helsinki.fi/en/research/doctoral-education/the-application-process-in-a-nutshell. The requirements for pursuing a doctoral degree at the University of Helsinki can be found at https://www.helsinki.fi/en/research/doctoral-education/eligibility-and-educational-documents. The chosen applicant is expected to reside in Finland while employed by the University of Helsinki.What we offer A salary of 2600 – 2800 €/month depending on the candidate's qualifications A professional, stimulating working environment The University of Helsinki also offers comprehensive services to its employees, including occupational health care and health insurance, unemployment and pension fund, a generous holiday package, sports facilities, and opportunities for professional development: https://www.helsinki.fi/en/about-us/careers. How to apply Applications should be submitted through the University of Helsinki Recruitment System via the "Apply now" button. Internal applicants (i.e., current employees of the University of Helsinki) submit their applications by using the Employee Login button. The application must include: A cover letter, stating your motivation, scientific background, documented experience with large-scale genomic analyses, and research interests A detailed CV with a list of publications and other research output, if applicable 2-3 references (name, institution, e-mail, telephone number, and relation to the candidate) These should be uploaded in .pdf format. Applicants may also upload their Master’s thesis or other representative research work, if available. Please submit your application through the web-based recruitment system linked in the announcement. Although the recruitment system includes a basic CV, we ask candidates to also include a separate, detailed CV in .pdf format with their application. Applications without a cover letter and/or detailed CV will not be considered further.  The closing date for applications is 31.5.2026. Inquiries about the position can be directed to Mariike Kuijjer: mariike.kuijjer@helsinki.fi. If you need technical support with the recruitment system or additional information about the application process, please contact HelsinkiUni Help, uni-help@helsinki.fi.  About us The University of Helsinki (UH), founded in 1640, is a vibrant scientific community of 40,000 students and researchers. It is one of the leading multidisciplinary research universities and ranks among the top 100 international universities in the world. It is currently investing heavily in life sciences research. UH offers comprehensive services to its employees, including occupational health care and health insurance, sports facilities, and opportunities for professional development.  The Faculty of Medicine promotes high quality scientific research. It provides research-based undergraduate and postgraduate education in medicine, dentistry, psychology and logopedics, and an international Master's Program in Translational Medicine. It also offers psychotherapist education. In addition to its teaching and research activities, the Faculty serves as a significant expert organization in the healthcare sector and contributes to the discourse on ethics in the field. The Faculty aims to be one of the best medical research faculties in the world, while rein-forcing its status as a distinguished institution of multidisciplinary education in healthcare.  The Faculty of Medicine at the University of Helsinki constitutes the academic medical center together with HUS Helsinki University Hospital and the Helsinki Institute of Life Science (HiLIFE). This medical center has been successful in international comparisons, ranking among the top 10 medical campuses in Europe and the top 50 globally. A diverse and equitable study and work culture is important to us. That is why we do our best to promote an inclusive university community. We encourage all qualified applicants from diverse backgrounds to apply for our positions. Click this link to read about accessibility and inclusivity at our University. JOIN US TO BUILD A BETTER WORLD – TOGETHER! #HelsinkiUniCareers

Semiconductor fabrication is one of the most complex and precision-driven forms of manufacturing. At nanometre scales, even subtle variations in process conditions can introduce defects that degrade device performance, reduce yield, and drive up production costs. Addressing this challenge requires new modelling approaches that can capture the full complexity of fabrication processes and enable optimisation before physical manufacturing begins. This project aims to develop advanced deep learning models capable of predicting fabrication outcomes and guiding fabrication recipe optimisation. By learning directly from experimental and process data, these models will enable a shift from iterative, trial-and-error fabrication towards predictive and data-driven manufacturing. We are seeking a highly motivated Machine Learning Researcher to join a multidisciplinary team of fabrication engineers and AI specialists at the University of Southampton, within the School of Electronics and Computer Science, working in the group of Dr Yasir Noori. In this role, you will work at the interface of machine learning and semiconductor engineering, developing models that predict post-fabrication device characteristics from process parameters. You will engage with complex, high-dimensional datasets derived from real fabrication workflows, including microscopy, spectroscopy, and electrical performance measurements. You will work closely with fabrication engineers to translate physical processes into machine learning models, design and train deep learning architectures, and evaluate their ability to generalise across different process conditions. The models you develop will not remain confined to the research lab, but will be validated experimentally and tested at an industrial scale in collaboration with global companies in semiconductor fabrication and electronic design automation. The position offers a rare opportunity to apply machine learning to an important technical challenge with substantial potential impact. You will also be involved in supervising PhD students and junior researchers and play a central role in shaping the research direction of the team. Your work is also expected to contribute to the development of innovative technologies with a clear pathway to commercialisation through the spinout company Deep Fabrication, to influence how semiconductor manufacturing is approached in practice. The role will provide you with deep exposure to nanofabrication processes, experience working with industry-relevant datasets and problems, and the opportunity to publish in leading journals and conferences. It is particularly well-suited to candidates who are motivated by applying machine learning to real-world systems where the underlying physics is complex and not fully understood. This position is offered for 24 months in the first instance, with the possibility of extension for a further 12 months. To apply, please submit your CV and a cover letter outlining how your experience and interests align with the aims of the project, and provide responses to the short-listing questions.