Future Tech Jobs
Future Tech Jobs
Performs bulk and single cell NGS protocols, spatial transcriptomics, and sample processing in a core facility. Operates Chromium X, Visium Cytassist, and Xenium platforms, with responsibilities including QC assessment, documentation, and customer liaison. Supports a research-intensive environment requiring precision, organisation, and technical adaptability.
Lead the development and validation of molecular assays including RT-qPCR, dPCR, and NGS for clinical biomarker programs across Moderna's therapeutic pipeline. Drive end-to-end assay lifecycle management, ensure regulatory compliance with GCLP standards, and collaborate with cross-functional teams to deliver high-quality genomic data. Mentor scientists and integrate advanced analytics and AI-enabled tools to accelerate discovery and translational research.
This role involves leading the development and validation of advanced molecular assays—including RT-qPCR, dPCR, and NGS-based methods—for clinical biomarker programs at Moderna. The scientist will drive end-to-end assay lifecycle management, ensure compliance with GCLP and regulatory standards, and collaborate across multidisciplinary teams to generate high-quality genomic data. A key focus is integrating genomics, bioinformatics, and clinical insights to support drug development and inform future pipeline strategies.
A Senior Computational Biologist will drive target discovery in immunology and inflammatory diseases by integrating multi-omics data (phenomics, transcriptomics, genomics, proteomics) to nominate and validate novel therapeutic targets. The role involves developing computational methods, collaborating with cross-functional teams, and translating complex analyses into actionable drug discovery programs using Recursion’s AI-driven platform.
Drive target discovery and validation in precision oncology by integrating multimodal biological data (phenomics, transcriptomics, genomics, proteomics) to identify novel therapeutic targets. Collaborate with cross-functional teams to develop scalable computational methods and build compelling data packages that advance drug programs. Develop criteria for target nomination and support early translational strategies using AI-powered platforms.
Develop and validate machine learning models to predict clinical outcomes from tumor molecular states using multimodal data including single-cell, spatial omics, and real-world clinical datasets. Collaborate with wet-lab scientists and clinicians to test in-silico perturbations and inform oncology drug development decisions. Build reproducible, scalable tooling within a cross-functional translational science team.
Lead the development of machine learning models to predict clinical outcomes from in silico tumor perturbations by integrating multimodal data including single-cell, spatial omics, and real-world clinical records. Collaborate with wet-lab scientists and clinicians to validate models and generate testable oncological hypotheses. Contribute to scalable, reproducible tooling and advance causal inference methods in a high-impact translational research environment.
This role involves analysing high-throughput sequencing data to investigate RNA regulation in innate immunity, with responsibilities spanning computational analysis of multi-omics datasets and potential wet-lab experimentation. The researcher will apply bioinformatics techniques to study post-transcriptional regulation mediated by the ELAVL1 protein, using methods such as RNA-seq, eCLIP, and SLAM-seq. There is also an opportunity to contribute to experimental design, conduct CRISPR-based assays, and support training within a multidisciplinary team focused on inflammatory disease mechanisms.
Lead a team of computational biologists to identify and validate novel oncology targets using integrated phenomics, transcriptomics, genomics, and proteomics data. Develop and scale methods for target discovery in oncology and immunological diseases, collaborating closely with biologists, data scientists, and engineers. Drive data strategy and present findings to stakeholders to advance drug discovery programs.
This role involves developing, qualifying, and validating molecular bioanalytical assays—such as qPCR, ddPCR, and RT-qPCR—to support clinical pharmacokinetic and pharmacodynamic studies within a GCLp-compliant environment. The scientist will work closely with cross-functional teams to ensure robust data generation, maintain regulatory compliance, and integrate emerging digital and automation technologies into workflows. A strong focus on reagent management, assay documentation, and continuous process improvement is central to the position.
Lead a team of computational biologists to integrate multi-omics data for oncology target discovery and validation, while actively contributing to data analysis and cross-functional decision-making. Develop biomarker strategies and patient stratification approaches to guide therapeutic development. Collaborate closely with biologists, chemists, and clinical teams to advance drug programs using high-dimensional datasets and scalable computational methods.
This role involves developing, optimizing, and validating molecular bioanalytical assays—such as qPCR, ddPCR, and RNA isolation—to support mRNA-based clinical programs. The scientist will work within GCL0-compliant (GCLP) frameworks to generate pharmacokinetic and pharmacodynamic data, ensuring regulatory compliance and data integrity. Collaboration with cross-functional teams and adoption of digital tools and automation are key to advancing clinical trial execution and analytical processes.
Lead the development of machine learning pipelines integrating multimodal data such as single-cell omics, spatial biology, and real-world clinical records to predict clinical outcomes from in silico tumor perturbations. Collaborate with wet-lab scientists and clinicians to validate models and generate testable hypotheses for oncology drug development. Drive technical innovation in causal inference and generative AI while contributing to reproducible, production-grade research tooling.
Develop and validate machine learning models to predict clinical outcomes from in silico tumor perturbations by integrating multimodal data including single-cell, spatial omics, and real-world clinical datasets. Collaborate with wet-lab scientists and clinicians to generate testable hypotheses and inform oncology drug development decisions. Emphasis on causal inference, model interpretability, and robust software engineering practices in a translational research environment.
