Researcher in functional precision medicine and cell painting

This is NTNU NTNU is a broad-based university with a technical-scientific profile and a focus in professional education. The university is located in three cities with headquarters in Trondheim. At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world. You can find more information about working at NTNU and the application process here.     Video: https://youtu.be/Xt-yHCN5QS0 About the Job Are you excited by the possibility of turning cellular imaging, drug-response data and cancer biology into better treatment predictions for individual patients? We have a two-year position for a researcher in computational high-content imaging analysis as part of the international EP PerMed project ERKetype. ERKetype aims to develop digital twins as complex biomarkers for precision cancer medicine, with a particular focus on cancers driven by dysregulation of the RAS/RAF/MEK/ERK signalling pathway. The project combines patient-derived cancer models, Cell Painting and other high-content imaging approaches, pharmaco-proteomics, multi-omics data, artificial intelligence and mechanism-based modelling to understand why cancer cells respond — or fail to respond — to targeted therapies. The researcher will be part of FlobakLab at NTNU (www.flobaklab.com), an interdisciplinary precision oncology group with expertise in both wet- and dry-lab research. In this position, you will play a central role in analysing Cell Painting and high-content imaging data from drug screens in cancer cell lines, patient-derived cancer organoids and patient-derived tumouroids. You will work closely with SINTEF Industry and its robotic high-throughput drug-screening facilities. The researcher will also have access to FlobakLab’s dedicated GPU workstation, equipped with 4 × RTX PRO 6000 Max-Q GPUs and supported by NVIDIA. Your work will include developing reproducible Python-based analysis pipelines, extracting and interpreting morphological drug-response signatures, and developing and implementing approaches to integrate imaging-derived phenotypes with mechanistic and knowledge-driven modelling frameworks. This is an opportunity to work at the interface between computational biology, cancer systems medicine and translational precision oncology. You will collaborate closely with experimental, computational and clinical partners in Norway and internationally. The position is well suited for a motivated researcher who wants to develop advanced imaging-data methods and apply them to clinically relevant questions in personalised cancer medicine. Your immediate leader is Professor Åsmund Flobak. Duties of the position • Analyse Cell Painting and other high-content imaging data from drug-screening experiments in cancer cell lines, patient-derived cancer organoids and patient-derived tumouroids. • Develop, run and document reproducible image-analysis workflows, including CellProfiler-based segmentation, feature extraction, image quality control and metadata handling. • Develop Python-based pipelines for preprocessing, normalisation, batch correction, quality control, statistical analysis, visualisation and interpretation of large-scale imaging and drug-response datasets. • Extract and interpret morphological drug-response signatures, including single-cell and population-level phenotypic profiles. • Contribute to identification of phenotypic mechanisms of action from perturbation screens and link these to drug response, cancer biology and signalling pathway activity. • Evaluate and implement advanced computational approaches for high-content imaging analysis, including cytomining workflows and, where relevant, CNN-based methods such as DeepProfiler or related deep-learning approaches. • Integrate imaging-derived phenotypes with other data modalities, such as drug-response data, pharmaco-proteomics, genomics, transcriptomics and clinical or model metadata. • Develop and implement approaches for connecting Cell Painting-derived phenotypes with mechanistic and knowledge-driven modelling frameworks, including Boolean, logic-based or related digital-twin modelling approaches. • Work closely with SINTEF Industry and its robotic high-throughput drug-screening facilities, as well as experimental, computational and clinical collaborators at NTNU and partner institutions. • Contribute to data management, documentation, reproducible research practices and sharing of analysis workflows and results within the ERKetype consortium. • Contribute to scientific publications, conference presentations, project deliverables and reports. • Contribute to coordination of NTNU’s scientific activities in ERKetype, including follow-up of relevant milestones, deliverables, consortium meetings and reporting. • Contribute to supervision and mentoring of PhD candidates, medical research students and other junior researchers where relevant. Required selection criteria • You must have a PhD or equivalent doctoral degree in a relevant field, such as bioinformatics, computational biology, biomedical engineering, image analysis, systems biology, cancer biology, biotechnology, computer science, or a closely related discipline. • Documented experience with analysis of high-content imaging, Cell Painting, microscopy-based screening data, or other quantitative biological image data. • Good working knowledge of CellProfiler or closely related bioimage-analysis software. • Strong programming skills in Python for scientific data analysis. • Experience with processing, quality control, statistical analysis, visualisation and interpretation of large biological datasets. • Experience with reproducible research practices, including documentation of code, analysis workflows and results. • Excellent written and oral English language skills Preferred selection criteria • Experience with Cell Painting, morphological profiling, cytomining, high-content screening or microscopy-based drug-screen analysis. • Experience with analysis of drug-response data from cancer cell lines, patient-derived organoids, tumouroids or other advanced cancer model systems. • Experience with Python-based workflows for large-scale image-data analysis, including feature extraction, quality control, normalisation, batch correction, dimensionality reduction, clustering or classification. • Experience with CNN-based, deep-learning or other machine-learning approaches for bioimage analysis, for example DeepProfiler or related frameworks. • Experience with integration of imaging-derived features with other data modalities, such as drug-response data, genomics, transcriptomics, proteomics, phosphoproteomics, CyTOF or clinical data. • Experience with mechanism-based or knowledge-driven modelling, such as Boolean modelling, logic modelling, network modelling, ODE-based modelling, model personalisation or digital-twin approaches. • Knowledge of cancer biology, precision oncology, pharmacogenomics, patient-derived cancer models or RAS/RAF/MEK/ERK signalling. • Experience with reproducible research practices, version control, workflow management, high-performance computing, GPU-based analysis or containerised analysis environments. • Experience from interdisciplinary or international research projects involving experimental biologists, computational scientists, clinicians or industry partners. • Experience with scientific writing, project reporting, deliverables or coordination of collaborative research activities. • Good written and oral Norwegian language skills Personal characteristics • Motivated by interdisciplinary research at the interface between computational biology, cancer biology and precision medicine. • Analytical, structured and quality-oriented. • Able to work independently, take initiative and drive tasks forward. • Collaborative and communicative, with the ability to work well with experimental, computational and clinical partners. • Curious and willing to learn new methods and technologies. • Reliable and organised, with