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Machine Learning Data Scientist posting
<h1><strong>Role summary:</strong></h1><p style="min-height:1.5em">At Differential Bio, our mission is to unlock the potential of the bioeconomy by fixing how biological R&D and scale-up are done.</p><p style="min-height:1.5em">We’re looking for a <strong>Machine Learning Data Scientist</strong> to join our growing cutting-edge modelling team. You will develop novel machine learning algorithms and mechanistic models of bioprocess systems and push them from research proof-of-concept into production-ready commercial use.</p><p style="min-height:1.5em">This role is focused on <strong>research</strong>, including creating <strong>hybrid models that combine mechanistic and machine learning approaches to predict how microbial fermentations behave</strong>, and that keep working when we move to a new organism, a new sensor, or a larger reactor. You’ll play an instrumental role <strong>on two existing grants</strong> and work with leading academic groups and industrial partners on pilot- and production-scale data. In addition, you’ll develop new research projects and drive them to completion and application on customer projects.</p><p style="min-height:1.5em">We’re interested in researchers who want their work to end up in real bioreactors and are excited about working at an early-stage startup. You should be comfortable owning hard, open-ended problems where nobody has the answer yet, making evidence-based decisions under uncertainty, and shipping real solutions.</p><p style="min-height:1.5em"><strong>How you’ll work</strong></p><ul style="min-height:1.5em"><li><p style="min-height:1.5em">You’ll collaborate with technical leads and academic/industry external partners to design and execute research that meets our commitments in both consortia and strengthens our own platform.</p></li><li><p style="min-height:1.5em">You’ll own your research projects from scoping to delivery, with support from our modelling lead but with autonomy and independence.</p></li><li><p style="min-height:1.5em">You’ll work closely with our wet lab, dry lab and software teams, and identify which data you need to generate from our robotic lab.</p></li></ul><p style="min-height:1.5em"></p><h1><strong>Key responsibilities:</strong></h1><p style="min-height:1.5em"><strong>Hybrid bioprocess modelling</strong></p><ul style="min-height:1.5em"><li><p style="min-height:1.5em">Develop hybrid mechanistic/ML cell response models that predict fermentation outcomes (growth, titer, yield, productivity) from process and sensor data.</p></li><li><p style="min-height:1.5em">Fuse time-series signals from process analytical technology (Raman, NIR, dielectric spectroscopy, dissolved oxygen, off-gas) with offline analytics.</p></li><li><p style="min-height:1.5em">Build mechanistic backbones (e.g., kinetic/ODE models, genome-scale metabolic models) that constrain learning and generate synthetic training data.</p></li><li><p style="min-height:1.5em">Develop machine learning surrogate models for computationally costly mechanistic simulations.</p></li><li><p style="min-height:1.5em">Quantify uncertainty so that models can be trusted in high-stakes production settings.</p></li></ul><p style="min-height:1.5em"><strong>Transfer across organisms and scales</strong></p><ul style="min-height:1.5em"><li><p style="min-height:1.5em">Design